{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d53e19e2-38db-479c-845b-25904afa715e",
   "metadata": {},
   "source": [
    "# 09. 基于分子指纹的化合物聚类分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feac060c-0a5a-421e-ad20-388c00b57a7a",
   "metadata": {},
   "source": [
    "- 聚类分析：聚类分析是一种将数据集中的样本划分为若干个簇（cluster）的过程，使得同一簇内的样本相似度较高，而不同簇之间的样本相似度较低。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3f153f-2dbb-456c-8a08-2dc752f262f8",
   "metadata": {},
   "source": [
    "### 9.1. 基本分析思路\n",
    "- 化学结构去重复：依据inchikey\n",
    "- smiles字符串转mol分子对象，然后生成每个分子的分子指纹，转数组\n",
    "- 基于分子指纹的PCA分析，用于绘图可视化\n",
    "- 基于分子指纹的聚类分析，投放到PCA图上，以方便观察分子簇\n",
    "\n",
    "**尝试**：将所过程数据，尽量存储在原始的数据框中，方便后续调用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77ad3f12-ceda-4dc8-aa64-483130154c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, PandasTools, rdFingerprintGenerator\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "\n",
    "IPythonConsole.ipython_useSVG=True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e45a66-4401-4580-b391-0c9cf43b2721",
   "metadata": {},
   "source": [
    "### 9.2. 载入和清洗数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b425744-6499-46ac-810c-b2aec5a40477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 47)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = 'data/LC-MS_2.csv'  \n",
    "df = pd.read_csv(file_path)  \n",
    "df = df.drop_duplicates(subset='INCHIKEY') \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9aedeb12-2d6b-4856-89ac-1b323a00f6ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mol'] = df['SMILES'].apply(Chem.MolFromSmiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a9fd4f6-bbd0-433c-92d2-47483ec1bf12",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_generator = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=1024)\n",
    "df['fp'] = df['mol'].apply(fp_generator.GetFingerprint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee7c675c-f1ae-4333-afd6-87d5c5596201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 1024)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_arr = np.array([np.array(fp) for fp in df['fp']])\n",
    "fp_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4343d245-8096-4b38-88e6-7d63c12302c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], shape=(144, 1024))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af879f9-c91c-42d6-8bf5-b3e2d3aaaa89",
   "metadata": {},
   "source": [
    "### 9.3. PCA 分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a95af0f0-d18f-40ba-9507-4862087a8d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "43a80ae7-c571-4257-9795-93c1de065b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 2)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. 聚类结果可视化（采用PCA将数据降到二维可视化）\n",
    "pca = PCA(n_components=2)\n",
    "fp_pca = pca.fit_transform(fp_arr)\n",
    "fp_pca.shape # 每个分子的特征值矩阵（fp_arr）被压缩成了两个数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9ef1091f-a4e3-4ed8-8777-30a28d8a02da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(fp_pca[:, 0], fp_pca[:, 1])\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('PCA of Molecular Fingerprints')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3766d3-cfa3-40b3-ada5-6d412895d493",
   "metadata": {},
   "source": [
    "### 9.4. 聚类分析\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e6217873-4062-44b6-bb27-7dbc47cd69b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5efe08c8-9a78-4a66-a3db-e83f3f5a382e",
   "metadata": {},
   "source": [
    "#### 9.4.1 K-means聚类分析\n",
    "\n",
    "- k-means 的基本原理\n",
    "    - k-means 的目标是将数据集划分为 k 个簇，使得每个簇内的样本尽可能紧密地聚集在一起，而不同簇之间的样本尽可能分散。具体来说，k-means 通过最小化簇内误差平方和（Intra-Cluster Sum of Squares, ICSS）来实现这一目标。\n",
    "    - 核心思想\n",
    "        - 质心（Centroid）：每个簇的中心点，通常由簇内所有样本的均值计算得到。\n",
    "        - 距离度量：通常使用欧几里得距离（Euclidean Distance）来衡量样本与质心之间的距离。\n",
    "        - 优化目标：最小化所有样本到其所属簇质心的距离平方和。\n",
    "- k-means 的算法步骤\n",
    "    - 初始化质心：随机选择 k 个样本作为初始质心（或使用其他初始化方法，如 k-means++）。\n",
    "    - 样本分配：计算每个样本到所有质心的距离。将每个样本分配到距离最近的质心所在的簇。\n",
    "    - 更新质心：对每个簇，重新计算质心（即簇内所有样本的均值）。\n",
    "    - 迭代收敛：重复步骤 2 和 3，直到质心不再变化（收敛）或达到预设的最大迭代次数。\n",
    "- k-means的优点\n",
    " - 简单易实现，计算效率高。\n",
    " - 适用于大规模数据集。\n",
    "- k-mean的缺点：\n",
    "    - 需要预先指定簇的数量 k，无法自动确定最优簇数。\n",
    "    - 对初始质心的选择敏感，可能导致局部最优解。\n",
    "    - 对噪声和异常值较敏感。\n",
    "\n",
    "总结来说，k-means 是一种基于划分的聚类算法，通过不断调整质心和样本分配来最小化簇内误差平方和，最终实现数据的分组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ca49f1d8-ee98-4d2b-b194-c2efca433894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "kmeans_labels = kmeans.fit_predict(fp_arr)\n",
    "\n",
    "kmeans_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b5015c03-3170-47ea-8f3d-eb23d7f55c38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 2, 2, 4, 4, 2, 2, 0, 2, 0, 2, 2, 2, 0, 0, 4, 2, 2, 2, 2, 1,\n",
       "       4, 4, 4, 4, 4, 2, 4, 2, 1, 2, 1, 1, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4,\n",
       "       1, 1, 1, 4, 1, 1, 4, 4, 4, 0, 1, 2, 2, 4, 1, 2, 4, 4, 4, 2, 1, 2,\n",
       "       1, 2, 1, 4, 1, 1, 4, 2, 1, 2, 4, 2, 4, 2, 0, 2, 2, 2, 2, 2, 1, 4,\n",
       "       4, 2, 2, 1, 4, 2, 4, 2, 4, 1, 2, 2, 3, 4, 3, 2, 2, 0, 3, 3, 3, 3,\n",
       "       2, 4, 1, 0, 4, 3, 3, 2, 1, 3, 4, 2, 3, 0, 4, 0, 4, 4, 2, 2, 2, 4,\n",
       "       4, 2, 2, 4, 0, 3, 1, 4, 4, 2, 4, 0], dtype=int32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_labels # 分类标签"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b788ff-97ec-4422-8765-92e48a47576d",
   "metadata": {},
   "source": [
    "#### 9.4.2. 层次聚类(AgglomerativeClustering)\n",
    "- **Agglomerative Clustering（凝聚聚类）**是一种层次聚类算法，它通过逐步合并距离最近的簇来形成更大的簇，最终构建出一个层次化的簇结构。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "44124857-2ba3-46cb-ae88-7f4e4b85612a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144,)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglo = AgglomerativeClustering(n_clusters=5)\n",
    "agglo_labels = agglo.fit_predict(fp_arr)\n",
    "agglo_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5abbf6ad-c9c5-46cb-b0b0-f1c5c847480a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 4, 4, 1, 1, 4, 2, 4, 4, 4, 4, 4, 4, 0, 4, 2, 4, 2, 2, 4, 2,\n",
       "       1, 1, 1, 1, 1, 4, 1, 4, 2, 4, 2, 2, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1,\n",
       "       2, 2, 2, 1, 2, 2, 1, 1, 1, 4, 2, 4, 4, 1, 2, 4, 1, 1, 1, 4, 2, 4,\n",
       "       2, 2, 2, 1, 2, 2, 1, 4, 2, 2, 1, 4, 1, 4, 4, 2, 0, 4, 4, 4, 2, 1,\n",
       "       1, 4, 4, 2, 1, 4, 1, 0, 1, 2, 2, 0, 3, 1, 3, 4, 4, 1, 3, 3, 3, 3,\n",
       "       4, 4, 2, 4, 1, 3, 3, 4, 2, 3, 1, 0, 3, 1, 4, 4, 1, 1, 4, 4, 0, 1,\n",
       "       1, 4, 0, 1, 1, 3, 2, 1, 4, 4, 1, 4])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglo_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416477c6-ece8-40f9-841a-8e45d04bed98",
   "metadata": {},
   "source": [
    "#### 9.4.3. DBSCAN聚类\n",
    "\n",
    "DBSCAN（Density-Based Spatial Clustering of Applications with Noise）是一种基于密度的聚类算法。DBSCAN通过识别数据中的高密度区域来形成簇，并将低密度区域标记为噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b91d36af-9bfc-421e-b072-5274d4b03a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "dbscan = DBSCAN(eps=0.5, min_samples=5)\n",
    "dbscan_labels = dbscan.fit_predict(fp_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d8ead7b1-8e1b-46de-8018-0215b56518d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83bec8dc-d406-41d0-8ca3-0c52305a6301",
   "metadata": {},
   "source": [
    "#### 9.4.4 聚类结果的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "66c22ebe-3e97-4894-911a-8ef47b88893a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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87iWXXILU1FQMHTq0+rEnT56MJ554QpU+BKqblGGCWCKi5sije/BH3nK8sXla9TGHx4HZu7/DzrKduKbTVYi1xob0HIkosDZs2KAuy3fv3t2vj7tt2zYVqEppgpCAs0pSUpL6My0trTpIltLNhx9+GN9//z2OPfbY6s+RTKv0768ZxP7nP//BSSedhEAydBArKeum+o9OvjMfRa5ilVKPtcQg0caetkTUePK75OPt+y/V1fRX4d/Iq8hnEEsU5iSADYSbbroJEydOxJw5c1TrUgloGxokJcF0aWlpveBUsrlHHnlkrWNS8hlohg5im6JytwP/FK7Bm5uno9BV2Zc2yZaIazpejU4xHWE1WUN9ikRkIGXuchS5inyu7yjdgYwotg8kCmdy2V6SYo3ZvGXaNxGrZgAsG8FquuqqqzB69Gh8/fXXKpCVUoH//e9/qgOCr4FTQu7funXrWmt1N9J7K3tothu7jGJ3+W48s/656gBW5Drz8Nja/yHHkRPScyMi47GaLNCg+VyPtTALSxTu5NK+BJsvvPACSkpK6q3ne2mDJTWqIjMzs9bGrrqk/vXaa6/F559/jttuu01NRBU2m039KZ0FqvTs2VMFq1KG0Llz51q3UPTiZxDrR+Xucnyx6yvoqJ/2d+tuzNuzAG7P/h8GIqIDkVKBfgl9va5FmiPRMrJl0M+JiIJPAlgJKAcNGqQ2Xa1fvx7//PMPnn322er61JqqAsv7779f3Veyp5JlrWnSpEmqRdfmzZvVMKn58+ejR48eaq1du3Yq+/vVV18hOztbZWFlk/3tt9+OW265BW+99RY2btyoPu+5555THwcbg1g/crgd2F7qe6DC5pLNKPc4gnpORGRsEqhe0u5CtIxIr3XcZrLh1q43szsBUTMhG6gkYJTWV5Ix7d27t6pNlUEHL730Ur37W61WfPDBB6oEQepcH330UTz44IO17iNBsXQokMB1zJgxqm3Xiy++qNakXOCBBx5QXQZatGiBG264QR3/73//i3vuuUeVHlR9ngTI0nIr2DQ9UNXCTVBhYaEaPyvTu6RdhL+Vucvw4oZXsLLgL6/rJ6QMwfj2l8BiYikyETVOnjMfWeVZ2FS8GSn2FHSM6aDq7c0ae2QTGV15ebnKhkogeLBTScP5az7YeI3RlJ8zJqe2OsVrECs1bSeln8gAlogOiWRc5dYjzr8tdoiIjIrlBH7WJrI1Lm9/Gaza/i4EEaYIXNfpWqTZ00J6bkREREThgmlBP4uyROH45OPQK76Xmqxjgkn1iE2wxjMLS0REROQnjKoCwGq2ItWcglR7SqhPhYiIiCgssZyAiIiIiAyHQSwRERERGQ6DWPKLCk+FajHm0T2hPhUiIiJqBlgTS4el2FWMzLIszNn9PQoqCtRkoYFJA1gPTERERAHFIJYOWamrFHOz5mHmri+qj60tWoevM7/F3T2moGVk7QlDRERERP7CcgI6ZPkV+bUC2JrZ2Q+3f4xSV1lIzouIiIjCH4NYOmR/F6z2ufZn/kqUuEuCej5EREQEFOUVY9uanfhnyXpsX7tTfRwML7zwAtq3b6/GyB599NH47bffAvp8LCegQ+bSXT7XdPmfrgf1fIiIiJq77O05+N9VL2PZ3D+rjw0Y1Re3vnYtUtsGbr/KRx99hFtvvRUvv/yyCmCffvppjB49GmvXrkVaWmAmljITS4esd3wvn2vdYrsi2hIV1PMhIiJqzoryiusFsGLpnD/x5NUvBzQj++STT+Lqq6/G5Zdfjp49e6pgNioqCm+++WbAnpNBLB2yJGsihqQMrnfcZrLhknYXIdoSHZLzIiIiao7ydhfUC2BrBrKyHghOpxPLli3DyJEjq4+ZTCb18a+//opAYTkBHbIYawzOa3M2+iYcgW8yZ6PIVYSesT1xcsvRSGGLLSIioqAqKShtcL20sOH1Q5WTkwO3240WLVrUOi4fr1mzBoHCIJYOS5wtTvWF7RHbHS7djShLpMrEEhERUXBFxzdcxhcVF15lfiwnIL9lZRNs8QxgiYiIQiSxRbzaxOWNHJf1QEhJSYHZbMbu3btrHZeP09MD1zOeQSwRERFRGIhNjFFdCOoGspXdCSaq9UCw2Wzo378/5s2bV33M4/Goj4899lgECssJiIiIiMJEatsU3PXBJLWJS2pgpYRAMrCBCmCrSHut8ePHY8CAARg0aJBqsVVSUqK6FQQKg1giIiKiMBKbGBPwoLWu888/H9nZ2bj33nuRlZWFfv36Yfbs2fU2e/kTg1giIiIiOmw33HCDugULa2KJiIiIyHAYxBIRERGR4TCIJSIiIiLDYRBLRERERIbDIJaIiIiIDIdBLBEREREZDoNYIiIiIjIcBrFEREREZDgMYomIiIjIcBjEEhEREZHhMIglIiIiCiOFxWXYunMvVq3LxNaduerjQFq0aBFOPfVUtGrVCpqmYebMmQgGS1CehYiIiIgCbk9OIR558Tv89ufW6mOD+rXH5ImjkJYSF5DnLCkpQd++fXHFFVfgrLPOQrAwiCUiIiIKA4XFZfUCWPHbii145KU5uP+WUxAXE+n35x07dqy6BRvLCYiIiIjCQF5Bab0AtmYgK+vhhEEsERERURgoLnE2uF5S2vC60TCIJSIiIgoDMdG2BtejoxpeNxoGsURERERhIDE+Sm3i8kaOy3o4YRBLREREFAbiYiJVF4K6gWxld4LRAdnUFUrsTkBEREQUJtJS4lQXAtnEJTWwUkIgGdhABrDFxcXYsGFD9cebN2/GihUrkJSUhIyMjIA9L4NYIiIiojASFxMZ1Kzr0qVLMXz48OqPb731VvXn+PHjMX369IA9L4NYIiIiIjpkw4YNg67rCDbWxBIRERGR4TCIJSIiIiLDYRBLRERERIbDIJaIiIiIDIdBLBEREREZDoNYIiIiIjIcBrFEREREZDjsExsgBRWFyC7Pxt+FqxBtjsYR8b2QYEtAhDki1KdGREREZHgMYgMgz5mPVza+hn+K1lQf06Dh8g7jMShxACItxphdXO52oLCiEHnOXFhMViRYE5BoS4BJYwKfiIiIQotBrJ+5dTd+zP6pVgArdOh4c/N0dI7phNYGCGKLKoowb88CfLHrS/U1iRhLDG7oPBFdYjrDYuKPjpGVuErhcDtg1kyIt8WH+nSIiIgajSk1P5PM5Zzd3/tcX7L3NxjB2qJ1mLFzZnUAK4pdxXhi7VPY69wb0nOjw8uubyrehOfXv4gpf92NqWsew885v6DQWRjqUyMiIj8pKC/HxtxcrMjKxKa8XPVxIE2dOhUDBw5EbGws0tLScMYZZ2Dt2rUINKbT/Myj6yrY8yXPmQcj1PN+vnOW1zWX7sLvuUsxrtUpQT8vOnwbijeoNyJyZUBklmfh1U1vYHjqMJzb9ixEW6JDfYpERHQYMouKMHned/hx29bqYydktMfUE0ehZWxsQJ5z4cKFuP7661Ug63K5cNddd2HUqFFYvXo1oqMD9+8KM7F+FmG2o1tsV5/rRyb2Q1Pn1l3IdmT7XN9Ssg26XhkEkXHkO/Mxfcvb1QFsTfOzF6g3L0REZFwF5eX1AlixaNsWTJk3J2AZ2dmzZ2PChAno1asX+vbti+nTp2Pbtm1YtmwZAskwQexLL72EPn36IC4uTt2OPfZYfPvtt2hqJJN1QcZ5MHl5aVvY09A+uj2aOqtmQ6vIlj7Xu8V2gaZpQT0nOnyl7lJkO3J8rm8p2RLU8yEiIv/KKS2tF8DWDGRlPRgKCgrUn0lJSQF9HsMEsW3atMEjjzyiovqlS5dixIgROP3007Fq1So0Na0jWuH/ek5Gh30Bq0WzYEjK8bij++1IsiWiqYu1xuDcNmd7XYswRaBfQt+gnxMdPpNmbnDdbrYH7VyIiMj/ipyOw1r3B4/Hg0mTJuH4449H7969A/pchqmJPfXUU2t9/NBDD6ns7OLFi1X62huHw6FuVQoLg3O51Ga2qS4Et3WdhHJ3OTTNhDhrLGwmG4xCAvArO1yOD7Z9iFJ3WXUm+brO1yLZnhzq06NDEGOOQdeYLlhXvL7emrzRyohsG5LzIiIi/4i12Q9r3R+kNvbvv//GTz/9FPDnMkwQW5Pb7cYnn3yCkpISVVbQ0G65Bx54AKESa41VNyOSsojjk49Fr7geKHIVw6yZEWuJRQLbMRlWjDVa9Sp++J9H1Pe0Zg/jf3W8UvUBJiIi40qJilKbuKR0oC45LuuBdMMNN+Crr77CokWL1BX0QNN0A+3Q+euvv1TQWl5ejpiYGLz//vs4+eSTfd7fWya2bdu2qlZD6mqJmqMcx178lf8X/ipcpbLrQ1KPR5ItWW1KJCKi4JO4ZvPmzejQoQMiIiIOuzvBlHlzagWyge5OIKHkjTfeiBkzZmDBggXo0qXLYX3NEq/Fx8cfMF4zVCa2W7duWLFihfqiPv30U4wfP161dejZs6fX+9vtdnUjov1S7MkY3mIYhqadwOlrRERhpmVsLJ4Zc4raxCU1sFJCIBnY+MMMjg9UQiCJxVmzZqlesVlZWeq4BKKRkYEb8GSoTGxdI0eORKdOnfDKK68c1P0PNrInIiIiMmImNhR8dSyaNm2aar3lTbPLxHrbAVezXICIiIiIgitU+VDDBLFTpkzB2LFjkZGRgaKiIpW2lrqL7777LtSnRkRERERBZpggds+ePbjsssuQmZmpUswy+EAC2JNOOinUp0ZEREREQWaYIPaNN94I9SkQERERURPBrclEREREZDiGycRSaHh0D4oqitTfZXADWzIRERFRU8AglnzKdeRi8d4lWJjzo9p5eHzKcRicchzHzhIREVHIMYglnwHsY2v/h8zyyobF4vOdM/Fjzk+Y0v0OBrJEREQUUrw2TF6tLPi7VgBbJduRgyW5v6syAyIiIqJQYRBL9ZS6SvFTzs8+13/J+RXFrpKgnhMRERFRTSwnIK/j48ya2ee6rJngfcScUTg9ThRWFMGju2E32RFviw/1KREREVEjMBNL9USaI3Fi2nCf6ye2GI4YawyMKteZiw+2fYTJK/8P/145BY+seRx/F6xCmass1KdGRER02EpcJcgsy8TG4k3ILMtSHwfSSy+9pIZQxcXFqduxxx6Lb7/9FoHGTCx51TW2C3rEdsc/RWtqHe8U3RFHxPeGUeU78/H0uuewtXRb9bFd5Zl4fO2T+He3W9E7vldIz4+IiOhwN2a/sXk6/i5cVX1M/t2+ov14JNmTAvKcbdq0wSOPPIIuXbqobkZvvfUWTj/9dCxfvhy9egXu31VNl2drJgoLC9XI2oKCAvVOgQ4c8G0u2YJ5e+arH8phaUPROaYjEm2JMKq1Revw8D+Pel1rFdESk3v8G/FWlhYQEVHwlJeXY/PmzejQoQMiIiIO+XFKXCV4ccMrtQLYmoHsxE7/QrQlGsGQlJSExx9/HFdeeWWjv+aDjdeYiSWfEmwJONLWDz3jesj7HdjNNhjdP4W1M8s1SUbW4XYA1qCeEhERkV8UVhR6DWDFXwV/q/VAB7FutxuffPIJSkpKVFlBIDGIpQOym+0IF4lW31lkm8nW4IY2IiKipqzUXXZY64fjr7/+UkGrZFhjYmIwY8YM9OzZE4HEjV3UrPSI6+YzUD0hZTDirCwzISIiY4oyRx7W+uHo1q0bVqxYgSVLlmDixIkYP348Vq9ejUBiEEvNimRib+x8Xb1Atn1Ue5zSaiysJtYSEBGRMcVZ43xuvpbjgUzU2Gw2dO7cGf3798fUqVPRt29fPPPMMwgklhNQs2I1W9E7rhceOeJBrClahwJnPrrFdUMLexp7xRIRkaFFW6JVF4I3t7ylamBrdSfoMD5om7qEx+OBw+EI6HMwiKVmGcimmdOQFpEW6lMhIiLyqyR7kupCIJu4pAZWSggkAxvIAHbKlCkYO3YsMjIyUFRUhPfffx8LFizAd999h0BiEEtEREQURqIt0UHNuu7ZsweXXXYZMjMzVWssGXwgAexJJ50U0OdlEEtEREREh+yNN95AKHBjFxEREREZDoNYIiIiIjIcBrFEREREZDgMYomIiIjIcBjEEhEREZHhMIglIiIiIsNhEEtEREREhsMgloiIiIgMh0EsERERERkOg1giIiIiMhwGsURERERhRHcXQHdtgu78s/JPd0HQnvuRRx6BpmmYNGlSwJ/LEvBnIPJB13XkVeShsKIQFR4XEqzxiLPGwW62h/rUiIiIDEl3Z0Iv+D/A+dP+g7YhQPyD0MwtA/rcv//+O1555RX06dMHwcAglkLC5XFhS8lWPLvhBRRUVL5DNGtmnN7qVIxIG45Ya0yoT5GIiMh4GdiCOgGscP4IveBuIP5JaOb4gDx3cXExLr74Yrz22mt48MEHEQwsJ6CQyHXm4dG1T1QHsMKtu/H5zplYXbg6pOdGRERkSPre+gFsFeePlesBcv311+OUU07ByJEjESzMxFJIrMj/E06P0+vajJ2z0D2uO+KtcUE/LyIiIsPyFB3e+iH68MMP8ccff6hygmBiEEshsaVki8+13eV74Pa4gno+REREhmeKPbz1Q7B9+3bcfPPNmDt3LiIiIhBMLCegkOgU08nnWqvIlrCY+P6KiIioUbTkyk1c3shxWfezZcuWYc+ePTjqqKNgsVjUbeHChXj22WfV391uNwKFkQKFRJ/43ogwRaDcU15v7ew2Z6ouBURERHTwNNm0Ff9g5SYuqYGtYhsCTXUn8P+mrhNPPBF//fVXrWOXX345unfvjjvvvBNmsxmBwiCWQiLZnowpPe7ACxtewh5HtjpmN9lxbpuz0DWma6hPj4iIyJA0aaMV/2TlJi6pgZUSAi05YF0JYmNj0bt371rHoqOjkZycXO+4vzGIpZAwaSa0j26H/+sxGUWuIrg8btVWK96aACtLCYiIiA6ZpgLWwAStTQmjBQqpBFuCuhEREVF4WLBgQVCehxu7iIiIiMhwGMQSERERkeEwiCUiIiIiw2EQS0RERESGwyCWiIiIqAnweDxoLjx++FrZnYCIiIgohGw2G0wmE3bt2oXU1FT1saZpCEe6rsPpdCI7O1t9zfK1HioGsUREREQhJMFchw4dkJmZqQLZ5iAqKgoZGRnqaz9UDGKJiIiIQkwykhLUuVwuuN1uhDOz2QyLxXLY2WYGsURERERNgAR1VqtV3ejAuLGLiIiIiAyHQSwRERERGQ6DWCIiIiIyHAaxRERERGQ4DGKJiIiIyHAYxBIRERGR4TCIJSIiIiLDYZ9YImoSYwjzKvJRVFEIj64jzhqLBGsCzCZzqE+NiIiaKAaxRBRSFR4XNhVvwksbX1GBrIgyR+LSdhejX0I/RFkiQ32KRETUBLGcgIhCaq9jLx5b+7/qAFaUusvwyqbXsaNsR0jPjYiImi4GsUQUMh7dgx9zfoZLd3ld/3zHTJS6SoN+XkRE1PQZJoidOnUqBg4ciNjYWKSlpeGMM87A2rVrQ31aRHQYKjwV2FKyxef6rvJMODzOoJ4TEREZg2GC2IULF+L666/H4sWLMXfuXFRUVGDUqFEoKSkJ9akR0SGymqxoG9XG53q6vQVsJmtQz4mIiIzBMBu7Zs+eXevj6dOnq4zssmXLcMIJJ4TsvIjo0Jk0E4amDsGc3d/DrbvrrZ/Z5nREW6JDcm5ERNS0GSYTW1dBQYH6Mykpyed9HA4HCgsLa92IqGlJsaXg1q43I8YSU33MZrJhfLtLkRHVNqTnRkRETZemS4NGg/F4PDjttNOQn5+Pn376yef97r//fjzwwANeA+C4uLgAnyURNWaDV54zH4UVhXDDjQRrPOIt8bCaWUpARNTcFBYWIj4+/oDxmiGD2IkTJ+Lbb79VAWybNm0azMTKreaL0rZtWwaxRERERAYPYg1TE1vlhhtuwFdffYVFixY1GMAKu92ubkREREQUXgwTxErC+MYbb8SMGTOwYMECdOjQIdSnREREREQhYpggVtprvf/++5g1a5bqFZuVlaWOS7o5MpJjKYmIiIiaE8PUxGqa5vX4tGnTMGHCBL/WWBARERFRaIRdTaxBYm0iIiIiCgLDBLEUPtweNwpdRfLWBDGWWFhN/DEkIiKixmH0QEG117EX87MX4qfsn+GBjmOTj8HIFiOQak8J9akRERGRgTCIpaAGsFPXPIZsR071sdlZ32HJ3iW4u+cUpDCQJSIionAfO0vGIjXNy/P/rBXAVsmryMcvOYvV1CYiIiKig8EgloKi1F2GX/cu9rm+OPc3FLtKgnpOREREZFwMYikozJoJVs3qc91mssIE723UiIiIiOpiEEtBEWGOwKj0kT7XT2oxEjHWmKCeExERERkXg1gKmk7RHdEvvm+9491ju6FXXI+QnBMREREZE7sTUNDE2+JxRYfx2Fm2Ez/sWQgdOoamnoCMqDZIsCWE+vSIiIjIQBjEUtADWbl1i+2mPjabzKE+JSIiIjIgBrEUEgxeiYiI6HCwJpaIiIiIDIdBLBEREREZDoNYIiIiIjIcBrFEREREZDgMYomIiIjIcBjEEhEREZHhMIglIiIiIsNhEEtEREREhsMgloiIiIgMh0EsERERERkOg1giIiIiMhwGsURERERkOAxiiYiIiMhwGMQSERERkeEwiCUiIiIiw2EQS0RERESGwyCWiIiIiAyHQSwRERERGQ6DWCIiIiIyHEuoT4CIiIiIgiO/oBR5haVwOFyIi41AUkI0IuxWGBGDWCIiIqJmYEdmHu576ius3bhbfWyxmHDO2CNx0RmDVDBrNCwnICIiIgpz2blFmPTAJ9UBrHC5PPjwy2X4ct5fcLvdMBoGsURERERhbseuPGRlF3pd+2Dm78jJK4HRMIglIiIiCnNbduz1uVZc6kC5wwWjYRBLREREFObatEz0uRYZYYXdZrxtUsY7YyIiIiJqlHatk5GcGI29XsoGzjn5KCR72djldLqwN78EJaVOREZYkBgfhahIO5oKBrFEREREYS4tJRbP3H8eJj8yAzsy89UxTQPGDuulglir1Vzr/rn5Jfj4q2X4+Os/VDBrMmkYenQX3DhhuHqspkDTdV1HM1FYWIj4+HgUFBQgLi4u1KdDREREFFR784qRV1CK0jInEhOiVXY1Jqp2dlWC1jc//gXvzvit3ucf2bMN/vvv05AQFxXyeI2ZWCIiIqJmIjkxRt0aIiUEkoH1ZvnqHaokIZBB7MHixi4iIiIiqtWtQLKxvmTuKUBTwCCWiIiIiKpF2q2qBtaXpjLdi0EsEREREVWTOtnBAzvBm5Zp8UhLbhobuxjEEhEREVG16Cg7Jl0xAkd0b1UvgH3i7rORktRwTW2wcGMXERERkZ9I06fs3MoOAC6XG4nx0ao/q9GGCaSlxOHhO85Qm7h27clXfWTTkuOQmtw0AlhhrFeUiIiIqImSoHXNxt34v8dnVQ8VsFnNuOK843DqSX0QHxsJo5UVJMZHoXP7VDRFLCcgIiIi8oM9OUW4+f6Pa03Fcla48fJ7P2L5qu0hPbdwxEwsEVEYX9bMKi5GZnERChzlaB+fgOSoKMTZI0J9akRh6aelG+Hw0ZrqjQ9/Rp/urZvMzv5wwCCWiCgMuT0erMreg6u+mIGcstLq42d264HJg4ciNZr/kBL525oNWT7XtmfmweXyBPV8wh3LCYiIwpBkXy+Z8UmtAFbMWPsPPly1Ei63O2TnRhSuenZt6XOtXeskWK3moJ5PuGMQS0QUhv7cnYVip9Pr2hvLlyG7dH/NHhH5x3H9OyIywup17eoLB6tNUuQ/DGKJiMLQprxcn2uFDgecbl7WJPK3FilxePaB85GeGld9LMJuxc1XDFf1sORfrIklIgpDfdLSfa6lx8QgwsJf/0T+Zjab0KNzOl5++CLkF5aiwuVBYlyk6hNrtfK/uZBmYisqKnDHHXegc+fOGDRoEN58881a67t374bZzHoPIqJQ65qcglYx3kdD3nbM8Ujjxi6igJGJVp3bp6mANj0tngFsUwhiH3roIbz99tu49tprMWrUKNx666245ppr6rV0ISKi0GoZG4t3zzoX/dP3j42Msdnwf0OGYUSHTtA0LaTnR9Scud0eFBSVodxREepTMTRNb0TU2aVLFzz11FMYN26c+njDhg0YO3YsBg8erLKye/bsQatWreBuorteCwsLER8fj4KCAsTF7a9XISIKV3llZcgtK4PD7UK8PUJlYK28YkYUEtJia+fufMz9cTVW/rMTqUkxOGvskWiZGo+kRF4daWy81qggNioqCqtXr0b79u2rj+3cuRMjRozAwIED8dhjj6Ft27YMYomIiIjqWLtpN2667yOUlNbuHHLT5cMxZmhPxO0bS5ubXwKX26NG1ibE7e9oIIMUJIMrV1IS4iJhtYTnG9KDjdcaVaSRnp6OjRs31gpiW7dujfnz52P48OGYMGHC4Z01ERERURjK3luEJ1/9vl4AK154awGO7tceHl3H0j+34o2Pf8Hu7EK0a5OMay8egh5d0lFUXI53Pl+ChYvXq/1HJw/vhbPHHokWNTohNDeNCmIl4/r+++/jxBNPrHVcSgh++OEHDBs2zN/nR0RERGR4RSUOrFqf6XXN7dGRk1uEhUvWw2a14Ibxw1S9bGSEDctXbUdOXjF++n0jfvxtQ/XnvD/rd3X/5x44H2kp3jdxhrtGBbH33HMP1qxZ43VNMrILFy7E3Llz/XVuRERERGHB42m4N3N8XBS6d2qBl979Ees376k1QKFX15Y4aUiPWkGs2JmVj6V/bcXJw3ujOWpUd4J27dph9OjRPtclIzt+/Pjqj0855RRkZnp/13EoFi1ahFNPPVU9j9SDzJw502+PTURERBQoMVF2tG+T7HXNbqvMKT47bUGtAFb8smwT5v28Vm0C82bOotUoK/c+nS/cBXRilwSdZWVlfnu8kpIS9O3bFy+88ILfHpOIiIgo0KRf7C1XnagGItR1xui+KHe4sGXHXq+fO//XdbDbvV88j4qweX3M5sBQ3XelnZfciIiIiIxGygVefeRivPv5Eqxen4mUxBicf9oAHNGtNVat39VgX1lvG8LE2Scfpepom6Ow/qodDoe61WzZQEREROQPJaUO5BWUorTMiegoO5ISotRmLF/kPt06tsC/rx2F4pJy1ULLbrNib34J0pJ9b86yWExeh0mNGdYLHTNS0FyFdRA7depUPPDAA6E+DSIiIgrDllnPT1+A+YvXwePR1SX90UN74F8XDlFjZxsSFxOhbrIx66Hnv8Pvf27BzVcMR5cOafVqYsWYob3QtlUinrr3HHwzf5UKfseNPAJt0hORGL+/j2xzE9ZB7JQpU9Ro3JqZWBnGQERERHSoCovL8OjLc7D4j821Lvl/88MquF06bvvXiYiKtDf4GNIH9t7/fakGIIhpH/+K+yadgrc/X4I/V+9Qx0wmDScN7oEJ5xyD1KRYdRvQpx3HRjeHINZut6sbERERkb9ICUHNALamuT/9g8vPO/aAQaz0fq0KYEVhcTnuf+ornHPyUbjo9IEqUxsbE6Emc9Wc2sUANkBBrPRA++abbzBu3Dj18V133YWkpCR/PgUREVGTl1lUhL/37MaP27agdWwcRnXqjPSYWERaraE+NfKDgkLfnZektKC4ZP9+HF+27sj1OhBh2ie/qr+/8filPltykR+D2A0bNuDNN9/E9OnTkZ2djYqKiurL+f5UXFysnqvK5s2bsWLFChUoZ2Rk+PW5iIiIDsWOwgJc/Pkn2F5YUH3s8V9/wnNjxmF4+w6IYCBreDHREQ2uR0X63txVJTkx2uea2aQhwkdLLdrvkBuLSf/Xt99+GyeccAK6deuGX375Bffeey927Kis4wiEpUuX4sgjj1Q3IfWu8nd5XiIiolArdjox9adFtQJY4dF13Pzd19hTWhKycyP/kc1UXTukeV0b0CcDCfGRB3yM9NQ4tGoR73Vt+HHdmvWGrYAFsb///juuueYapKen4+mnn8bpp5+u6jNefPFFXHvttWjRogUCZdiwYarFRN2bZICJiIhCLa+sDHM2rve65vJ4sDzLf1MsKXQkwHzw36ejc7vUWsdlPOyU68cgLubAQWxifDTuuflk1XWgJtm4dfWFgw/qMZq7RuWq+/Tpo3b4X3TRRSrz2qtXL3V88uTJgTo/IiIiw3B53HB76edZJb+8PKjnQ4EjWdSn7z8X2XuLkVtQogYXJCdEI+EgM6hxsRHIaJWE/95+GnLzSlBQVKYeMykhGi3TvGdo6TCC2LVr1+L888/H8OHD0bNnz8Z8KhERUdiLsdnRISERm/PzvK4PaNkKzWkQQIXLjdjoCMOMRZUBBNIlQMRE271mQ+Xryi0oxV9rdsLl8qBvj9bo2aWl+jobKz42Ut3K0xNUiy6ppWX3gQAFsZs2bVKX7idOnKhqYi+88EJcfPHFfMGJiIgApEZH44FhJ+KymZ/WWxvarj3SY31PZQoXufklWLUuEx9+uVQFfCcc3QVjh/Vq0tlFKU3ctjMXz7z5A35fuRWSTO/Xsw0mXXWi6hBg2ReEFxWX48t5K/HSO4vUfapIW6xxJ/ZW07qk1OBgNnbVFGHnZr9Doene5pgdhB9++EF1JPj8889RXl6O22+/HVdddRW6du2KpkpKIeLj41FQUIC4uLhQnw4RHQS5/Lq7pBg/bdsKs6ZhSEY7FSjE2Ruf9SCqIv/07SouwrqcHGzJz0P31FR0TEhCi5iGJy0djBKnE6uz9+ChnxZi5e4sJEZE4oojj8K5PXsjLfrwH7+p9099+o15mPfz2lrHpc/pK1MvQuv0BDRFu3bn48p/v6NaXNUNLqc9cVl13aoE51MenQGPB8gvLK113zsnjsK7ny/BiYO747xx/Wv1dqXAxGuHHMRWkSd47733VED7xx9/oHfv3li5ciWaIgaxRMayt7QUzyz5Be/+9Wet49cPPBpX9OuPxEhufKDGk3/21uRk4+IZn9SqUW0bF493zjwHGfEJftvkVeaqgFkzISUqCmaTMS6pH45V63bhminve12TbOxt/xrZ5LKOchn/nc+X4PUPf/a6ftaYfrhhwjCUl1dg84692LQ1B3IBOi0lDt/88DcWLF6n7ndEt1bo27MN3p3xG/5z6ziMOL57kL+S8HGw8dph/xclT3Ldddep9lcSxEoHASIif1iRlVkvgBUv/L4EG3L3huScyPgks3/FFzPqbbKStli3z5mN/DLfjewbQ95ktYqNU9nd5hDAVk2r8mXez2tQWNT0NraVljvx6x+bfK4v/Wsb8gtKMWvuStx070f432vf44lXv8ddj81E984tcMbovup+UidbVRcrAwtksxcFVqP+q5I62C+++AJFRUVeo+Zt27bh8ccf9+f5EVEzVegoxyvLfve5/vrypSjbN1iFqDF2FxerQNabpZk7kVvunyCWvGiCW2isFrPqCOBLRstEbNuVh1fe+xFuz/6L17Kp6+V3f8Sgvu1VDWzPzunYvD1HrWXuKVTr1ISC2FdffRXPPPMMYr0Upku699lnn8Xrr7/uz/MjombK6XZjb1ntmrOackpLUeF2B/WcKDzkOxrOBpa7XEE7l3Bz0mDfnYtGDu6O+JimV8su5Q3nn9rf5/r4c49RJQK+zP3xHww7pgvGDO+FH36tLC3omJEMu5UTt5pUECu1r5MmTfK5LmtvvfWWP86LiJq5OJsdgzPa+Vwf1q4Dom2N2wFMJNrE+t4lH2mxIM5uD+r5hBPpcyrBal2yY3/8OcfA3sTqYat0bJuC8WcfU++4lAokxkVhd06hz8/ds7cIp4/qi3c+WwKns/IN0LUXn4D4ONbsB1qj3iasX78efftW1n74GoYg9yEi/5EpP7rugdXcvN7V2ywWTOh7FD5d/TfK6mTGJMg4o3vPZlNnSP4lm6zGdemGr9bX3kEvrht4NNKifV9apoZJsHrz5cNx0pAeqsVWaakTQ4/poj5uyi224mIjccHpAzDqhB6qxZZs9hrYt50aYGC3WdQkru27vPf+lR6xUi+7YvUO1fP1piuGo2tH7yNpyb8a9a+iy+VCdnY2MjIyvK7LmtyHiPyzM3997l68t3IFytwunNOjF/qlt0R6TPj3mazSNj4en513ER5Y+AOW7NyhyumGtGuPu4cMQxt2GKFDFB8RgXtOGK66Eby9cjlKKiqQHBmJGwcdi1O6dIOtmb1h9LfEhGgcP6CT6rPqcnsQE2U3xLAD2ZQlt3ZtkuutXXz6IMz9cY0KbmuSAPeMUX3h0XWce/JRKhhOSYw2xNcbDhrVYuuYY47BmWeeiTvvvNPr+tSpUzFr1iwsXrwYTRFbbJGRAtiHflyAmWtr7/TtkZKKN047s1kFsqKgvBwFjnJo0BAfYWePWPILqaneU1ICp9uFCItVZWCZ3SdvpEzgnw1ZeOTF77A9szIj26FtCu68bhRSEqKhmTS0SGFcEex4rVFvN6+44grceuut6NWrF8aNG1dr7csvv8RDDz2EJ5988tDPmogUycDWDWDFPznZ+GLtGlx11ACYmtGkPMmcyY3In6xmM1r7IaGRv6cA+dkFKCkoRVxyHBLS4hCbGN5DDZobm82iesA+/+AFKCwqg+7RVfb17c+X4Pc/t6JlahwuPeto9O7WCqnJzSvJEEqNHnZwySWX4P3330f37t3RrVs3dWzNmjVYt24dzjvvPHzwwQdoqpiJJaPUwN4y+2t8vaFyl2tdcgn003MvQGqYT/4hMoLMTbvx3/OexPoafUaPPXUAbnrxaqS0TgrpuVFgVLhcmPfTWjz43Lf11i47+2hccOoAVVZATXDYwbvvvouPPvpIjZeVwHXt2rUqmJXgtSkHsERGIZu4pAbWF4fbhRqtCokoRPJ25+PeMx6rFcCKX79citfueAelRew3G452ZxfhuekLvK69N/N35Bfy+x4sjQpi3W43Hn30UTz99NPYuXOnKilYtmwZZs6cqbKwRHT4pAvB2d17+Vw/uXNXJEby0jpRqO3NzMOWv7d5XVvw8S/I310Q9HOiwCssLkeBjzcosvFrR5b3LgYU4iD24Ycfxl133YWYmBi0bt1aDTe4/vrrA3BaRM3bkS1bqk1cdSVFRmJCv6O4e5qoCcj10XJJeNwelBYzIxeOzKaG9yPYbU2zFy6aexD79ttv48UXX8R3332nsq+ymUsGIHg8HK1G5E/SfUC6ENx53BBVAyu7pif0PRKfn3eR+piIQi+5gZpXs8WM6LiooJ4PBYfUu7Ztleh1LTrKhhYp3NgVLI1K52zbtg0nn3xy9ccjR46EpmnYtWsX2rRpE4jzI2rWgezV/QfirB49VQ2slBAwA0vUdCS3TETXAZ2wbunGemujxg9FYgu+4QxHMrTh7hvHYtIDn6CsvKJWhlaOpyZx022THXYQUafNjdVqRUXF/m8iEfmPtNFiFwKipikhLR73fnIbnrjiBayYv0odM5lMOPHiwRj/wAWIiGbteriSiVxvPHYp5v2yFms2ZKFty0ScPKIX0lLimuxoXTT3FlvyH+fYsWNhrzFXWkoKRowYgegaY/o+//xzNEVssUVERP5WmFuE/D2FKCsqQ0xijMrARrHFUrPhcFQwcDXCsIPx48d77RtLRERU056SYuSVl0vPOCRERKJFTPheUYhLilU3ap4YwIZOo4LYadOmBe5MiIjI8JxuN1buzsLtc7/FtoLKFlOtYmLx6MjR6N+yFSKs/AefiPyDQ6KJiMhvdhQW4JIZn1QHsGJXcREmzPoMW2scC2c5pSXIKipCvmSi6YBkKMSuDVnY8Mcm9SeHRNDB4lZnIiLyiwq3G++uXKGysXW5dR2vLPsND404CZFhmo3dW1qKX3dsw3O/LcauokL0SEnDHccPQffkFMTU2EtC++XszMWr/34bCz/+VbXrlL03w84/Dlc/dinH9tIBMRNLRER+UVpRgeVZmT7XV+7ZjZIKJ8JRsdOJ1//4HTfN/hrrc/eipKICSzN34rxPP8RP27fBc/B7qJuN4vwSPH/jG5j/4c/V/eblzx8++AkvTpqm1okawiCWiIj8IsJiQfsE703gRdu4OERYrGFbQvDa8mVe1+5bMA+7S4qDfk5NXf6eAvw88zevaz99vkStEzWEQSwREfmF3WLBlUf297l+/cCjEWOzIRxtysvzmW3NLi1BAetj6ynK8x3YS/fPojxmYqlhDGKJiMhv2scn4ImTxsBeY7qc1WTC/UNHoGtSCsJVpKXhLSYWE/+5retAY3mj49hrlxrGjV1EROQ3soHp5C7dMLB1G2wvyFcbutrHJyIlKipsN3SJjPgERFmtqi64rh4pqUiMYEBWV3xqHHod3x2rfl5Tb6334O5qnaghfGtIRER+r41tGxeP49q2w5CM9mgbHx/WAaxIjYrCs2NOgVnTah2Ptdnxv1FjkRzVcNaxOYpPicOUd29C1wGdah3vNrAzJr9zk1pvjGKHQ3WFyCwqQrnL5eezJcOPnTU6jp0lIqJAKa+oUD1xZ675B+tzc3BMmwyMaN8RbeLioNUJbmm/vD0FyN9dgNzd+UhqkaDG9iakxR/057s8HmzOz8NjPy/C/C2bVenGmd164PpBx6BN3ME/DhkvXmMQS0RE5GdVPU8p8Dbn5eLUD9+tV8ohk+I+PvcCtIrlv/fhGq/xvzAiIgoqp8uF/PKysL7kywA2OORn6JVlv3utRZas+E/btobkvCg4uLGLiIiCdrl9W2EB3ly+DKuz96BTUjKuOmqA6mgQHaattyiwpHXZwq1bfK5/s2EdTuvaHRFhXpPdXDGIJSKigJMeqr/t2okrv/hcdSwQf2fvwRdr/8EzY07BqE5dYDObQ32a5AdSpSjDHUqcTtjMlkZ1pnB7PDA3Iost942127DbR0vZBHsELPy5ClsMYomIKOB2Fxfj9rnfVgewVeSjKfPm4Mj0VmjNvQqGl19ejh82b8SjP/+ohjxIj2DJhN567GC0jI31+Xk7Cwvxy45tmLdpo/o5OK9nb7SOjVMt2xoiAfIV/frjrh/mel2/rO+R7NEbxhjEEhFRwOWVlyGntNTrWklFBfaUFjOIbWKkbjm3rAwVbjfi7BFoERMDUwNdFiQDO3/zJtw+d3b1sQqPB5+tWY31ebl4bdwZSI2Orvd5W/PzcP5nH2FPyf506rQVf+DhESepADjqAKUmIzp0xNB27euVFfzrqIHo0MAYZDI+BrFERBRwB2qD03z65BjDprxc3Dn3OyzL2qU+To6MxN1DhmN4hw4qoPVGSgge/WWR17WVu7Ows6iwXhBb7HTgwR8X1Apgq9w9/3sc26Yt2h0giE2LjsHjJ43BtoJ8fLt+PSKsFpzSpRvSY2KQwCETYY1BLBERBVxSRKQKhPaWldVbk0lXLaJjQnJeVJ8MDLjgs49qZc7l+3bLnG8w7bSzMLR9B98ZdS/BaJW/9+xGv/SWtY7llZWr3q4+66h37kS7g8impkRFq9tRLVsf8L4UPlgoQkREASeXoh8bOcbr5egHh4/0epmZDkz3FEJ3Z0H35PrtMZdnZvos/Xjk50XYW+o9ULWZzA1uzpPMaF1u3aOCVV9KKpwHdc7UPDGIJSKigJPg9Zg2bfHVhZfijG490C05BWM6dcHM8y/GyA6d2JmgkXRPMXTnMuh510HPORV67hXQy+dB9+Qd9mP/tnOHz7W1e3PgcLt9brKS76030VYruqek1jsuY3m9Ha9ydJu2B3XO1DyxnIDIYHJKS7CjsBCLd2xHQkQEjm2TgbTo6LCfTU/GJz+jErA8NOIk1ZxePpZSAmocXXcDjh+hF9y8/6CrAHr+RCBmEhA1AZop6pAfv2NSks81+V1j1rznv+T7efPRx2F97l4sz8qsFcBOP/1spMfU706QHBWF/w47UZUv1O1coepaWWZCDeDYWSKDtSm65buvsbhGpsSsaXhq9MkY0aETAwKiZkB3Z0LPOQPQvWVdLdBSvoNmOfQM5taCfIx+dzqcXjKu9w8dgUv79IPWQJcCeaO9s6gIq/bsVmUk3ZNT0CIm1murq0JHOfLKylTN7fO/LcayzJ1IjoxSQzBO6tiZZSbNVOFBxmvMxBIZhLS5ee+vP2sFsEKyF5O++wZzL5mADom+MyjUfOSWlWJvaRnK3S4k2iNUIGC38Nd92PDk+whghQtwbwcOI4htGROLN087C9d+PQvFzv01qef27IWTu3RtMICtucmqb4v0Bu8nwe6zS37Fu3/9iSNbpOOJUWNVLe7W/Hws2bUD8iyDM9qjbXz8IX8tFN74W43IIOSX+1t/Lve6Jhsj5m3ehKsYxIYNXXcCegWgRdUKGsoqKlDkcKgNLwUOB2JsNrXrPzGy8vLx5rw83Dz7KzUNS0RYLLhuwNG46Ig+SNp3Hwrzf7o13y2pnE4X9uaVoLC4HHa7BQlxkUiIq/1zIfXJg1q1xrcXjVdZWWmD1TkpWWVI4yO8t9c6FD9s3qQCWDF15Ghc+cUMbCnIr17/7J9VKhB+dswpaBuf4LfnpfDBIJbIICRQLXI6fK5nFhcF9XwoMHR3PuDeDL3kbUDfC9hHABEnocSVgi35eShyOvHOyhX4buP66t6r/dNb4akxJ6taxYtnfIys4uLqxyt3ufDk4p+RFBmJC3r3abBZvZFVVLiQv7sAHrcHkTERiEv2PR3K8EwJgLmD+jmpR4sBzK28flp+YSk+/3YF3p35mwpmRbdOLXD/pHFo26p2GysZ1SrDJwI1gCK7pAQvLl2i/n55v6Pwzfp1tQLYKn/uzsIfmZkMYskrdicgMogoqwVHpLXwuX5CRvugng8FqF1S6VvQc88HHF8DzsVA0cNw5D+FOZvW4f2/V+Lj1X9jdo0AVkhD+snfz8GanOxaAWxNzyz5FXtKvK8ZXc7OXEy/+wNc2XMSLulwHe4eNxX/LF6H8lLfb/qMTDOnQkv4n8rS12aGFv8UYKq/29/j0fH9T2vw5se/VAewYu3G3bj5/o+xZ29w3wS7PB5kFlU+57B2HfDV+rU+7ysZ2YLy+v2FiRjEEhmEXC6+54Thqk6sLhmt2C0lJQRnRX7l3g2UvFDvcDbOwd3zf8Cw9h3xtY9/7KW8YNW+EgJvZI69w7U/eAkXuVl5uO+sx/Dx41+grLhcHftnyXpMGnwPNv1ZewxpWLH0gJb8BRDzb8A+HIj6F7SUrwHbMdC0+hs8c3KLMf2TxV4fSgLYLTv2IpikzKVHatrB3Vn90gvPKwh0eBjEEhlIz9RUvHfWuarHZlXt2tk9euHtM87x2r6GjEV3ehvZacOuEosqC3B53CqD5evybPsE35dc4+0RqiTl+00bsXDLZmwvLFCPaXQ71mVi3e8b6x33eDx44eZpKMgpRDjSNDM0SwZMMVdDS3gOWuxt0CwdoZnsXu/vkHKLQu8DDMTGLdkIpsTISEw+/gT19/lbNuG0rt193vecHr38WotL4YM1sUQGEmW14Zg2GXj3zHPVrmFpWSO1juwRGyZkM5f3BfX/FpNZfc+9BbK7iovQIyVVBasFjsqMZE2X9emHBxb+gEXbtla/AZp64iiM6tgZ0QeYTd+U/f7dCp9r65ZuVNnZ+JTwbqmoNbCRq4rNakZ0lA0lpd5/xtq2qr0pVN7wSEu/QocDVnPl75mEiEj4U6/UNLwwdhzuXzgf75x5Dmat/Qeb82t3XTgqvRWOTPde40vETCyRAUmD8HYJCWrTBQPY8KHZKzNTtTnRKtoDu9mChVs34+TOXb1+rgwRSIyIVJl6aZFU/Zj7WiNJm6If9wWw6lHdbtw251uVkTWyhFTfAao90gaTmf/MiZTEaFxw6gCva3ExEejSYX8dbUF5Ob5Y+w9O+/AdjH3/LYx8Zxqu+WqW6nzhT7F2O8Z07opZF1ys3py9cdqZuHvIMPRpkY7+LVvh0ZGjVA9sttgiXzjsgIioidA9udALHwTKv6p13GEZi692X6HqYp8ecwpmrfkHczdtqN7cJW2Inhs7Dm3iKv+xzyouUuUFxRVOpEXFYP6WjXjk5x+9zqi/oNcReGDYibA2MPZVehQ3tB5KO9btwhU9JsHbP2Vn3DgWVz92KWx2vtETufklePm9H/Ht/L9R9XK1SInFo1PORKd2qdWt3KT11VVfzvA6revz8y5Cq9jA/vu5RzqtaBrSOK2r2SrksAMiIv+onChUqnq0Sm2ezIiX0g5/00xJQOxdgP1E6CWvA3o+YDseEdFXYkxsOromp+H1P5biyPSWuOLI/irLWnk+0epybxWpj66qkZaOBNNXLPcawAppa+T0EqQ63S7sLCzEjDWrsWZvjrqsO7ZLV7SOjfM6eSlUklsl4d/Trsfjl79QK5Dt2Kcdzrv9dAawNSQlROOmy4fjsrOORvbeIkRF2ZGUEIXUpNhaAwge+Xmh18/fU1KClbuzAh7EprG+nw4Sg1giogZsLyjArd99o9pYCQngzuvVGzcPOi4gIzE1cwoQeQpgO05yoIAWB80UgVgL0Dc9Eo+eNAblFRWIstnUDu8DibbacERauqqZ9WZQqzb1Hkdqbn/buRNXfPF5df2tbAh77rdf8cHZ56vLvU2F9IQdfNbR6HF0F/zy5VLVrWDgmCOR1qUFsmwVKMrLVeU3UitMQEyUXd3atKzdF7aKw+XGhtxcn58vPxdSAnCw3C439u7Kg9NRoco7klomwNxEs/pkPAxiiYh8kMzTlV9+XusfdQnq3v9rJWKsNtx6zPGwBWicq2b2HmREWa3qdrBk09aNRx+DuZs31MvGRlosOLN7T5jrZFYle3vDt1/W20BW5nLh5tlf46Nzzm9Sl3olkG3TrRXO63YackpKMGXeHMybNbd6/aQOnfDA8BPZweMgyM9CSmQUcsq8dzLomOj959Kb3Kx8fPPaXHz65FcoKShV9csX3X02hl9wPBJSWedKh6/pXBMiImpiZAqar6zU2ytXYE9pCYxA+gi/dfrZqhSgirRp+/Ds871OZJLgXXaleyNjSHPLmmbj+b2lpbj6q5mYt2VTreNzN2/EQz8uQLGPr4kquT0eWE0m/Kv/QK/r0tHihHbtvU5Lk3pbGWVbRYJWGUDx1n0fq7+L/OxCvHjzNMx6YTac5b46cRAdPGZiiYh82OZlDGYV6bFaWlEBI5AOFsdntMOn516o2m/J6FnpZCCX2b2RetiG+OpVG2pStyxjSr35dsN63HbsYMTYvfdRbe5kepbUP3+8+i/cdszxqm/rF+vWVK/H2Gx45ZTTa2Wz3W4PMvcU4LNvl2PJii2IjbbjotMHok/31ijNLsTsafO9PtfHj32B0eOHI73DQQ47IPKBQSwRkQ81M5feslKRFmNtGmoRE6NuspFLSgZ+3r5V1UDKJWLZrBZjqwzw0mPiVEauwkuwKrWlNTeRNSXZpb6b+UspRZGT2T9fAexNs79Sb8zkdsucb3H1UQMw/fSz1GuWGBGBdvGJaBEdDYvZjJJSB8ocThQWOXDtXe+jtGz/6/p/j3+B0UN74qJRfbx2jBCShS3KLWYQS4eNQSwRUQNBbNu4eK+9VM/t0VsFfkYjHRZ+2rYVk777WtW4CsnMXjtgEK7odxSSIqPU1zXpmOPw+C8/1fv8+4YOR4smVA9bU2oD3w/5GmMNPNQhkKQjwfm9jsCW/Dy1CbBlbKxq07Zs1y71vR7TqYuqlXU4KrBp21688eHP6NI+Fas3ZNUKYKt8t3A1zhrVV23kcnhZF7ZIfi+oGdbEvvDCC2jfvj0iIiJw9NFH47fffgv1KRFRmJKs5VtnnI0uScm1jsvAgRsGHWPIQRM7iwox8ZsvqgPYqizli78vwdJdO9XHsnHsgl598PqpZ6B3ahri7HYMaNkKH5x9Hk7s0KneRrCmQoJvaT/mjXzPjPimI9A25eVi4tdfYPK8OWgbn4D1uXtx/Tdf4sZvv8LP27chJTpaBbni73WZuOqOd/Dz0o2qr+zvf27x+biLV2zBwLFHel3r2r8jEtLYq52aWSb2o48+wq233oqXX35ZBbBPP/00Ro8ejbVr1yItjZcliMj/2ick4t2zzlWbhoqcDiTvy1TGGbBlk1ze/XjV3z57xj7322IMaNVaZWNltv2IDp3QL70lHG63Kp1IaOLz6+W8nx0zDrfP/RZLdu6oPj66YxfcNWRYdblEuCtxOpFVUozvN25Qmw+Hte+Arkkp6k1ZTbllpap9nLRfu27AICzaugXfblhXvf5H1i5c+/Us9WbG7jHjsZfnwOPRa2W33T5+lixWM6565GKsXLgahXv3t3dLbZuCKe9NCvtRwBQchgpin3zySVx99dW4/PLL1ccSzH799dd48803MXny5FCfHhGFqdSoaHUzOpfHrTJvDWVppV62bmBoJNJt4cVTTlNvOoqdTpVFlpZRcT4CcCmvqNrsJkMj5E8jK3U6MXvjetwxd3b1RLdpK/5Q3SjePO0sVSpQRbpMrNyzW/U+PqplK7y41PuVzQcWzsdb487Czqz9Gx2Xr9qOY/t3xE+/b/T6OYMHdEKrjBS8tOwxbFq5FdvX7ETHvu2Q0aMNUtvUvrJBFPZBrNPpxLJlyzBlypTqYyaTCSNHjsSvv/7q9XMcDoe61RxjRkTUXFnNFgxq3QY/1GlBVaVnahqiDLZZzRvpvCC3hkg2emt+Pl5etgTzNm9SAx8uOaIfzujew9D9ZHeXFNcKYKus3ZuDl5f+hv8bMrS6t7Fs4hKtYmOxvoEBB1ITXuap3bFi9sLV+O/tp+LvtbuQX1i75dr54/ojNTlGjbFNy0hRt2PG9ffb10hUpWkWNnmRk5MDt9uNFi1a1DouH2dleW+pMnXqVDV7t+rWtm3bIJ0tEVHTNLZzV9UuyRtpQeUrY3kgFW636sPqqpPJbaokgD3jo3fxyepVKiO5q6gIj/3yI675apYKBI1q/pbN9QLYKp/883etIQZSHmI3W1QwG32A+m67xYKeXfZPapMNXU+//gP+78axuOTMQejdrRWOH9AJzz5wHi47+2jExTTNDhYUXgwTxB4KydoWFBRU37Zv3x7qUyIiCvnldhlyIJeXq6RFR+OVcaeja50NbAejtMKpsnz3L5yHK7+cgUd/+VGVLNQtS2hKpITg+d9/9dpy6689u/FPdjaMKrfc9yAKCVZr1kNLicw1Awaq4RZSZiBt47w5vm0GkqOjMHniaDWytsr2zDz8+6HP0a9XGzwy+Qw8cOs4HNU7A/FxxipBIeMyTDlBSkqKmre8e/fuWsfl4/R073O87Xa7uhERUSWp+ZSygXfPPBd55WVqcEFCRKTqASqXfxtDAtUft23FdV9/UZ39+33XTryzcgXePuMcVbrQFOWXl2PuJu+1nOLzNaswtF37Rr8eTcEJGe1Upwlv+qS1qJVxlezqpUf0Q6I9AjP+WYV7TxiOexfMqxfo/mfYSNUfOC7DjjefuAwLF6/Dsr+2oVWLeJw+qi9apsUjii2zKAQME8TabDb0798f8+bNwxlnnKGOeTwe9fENN9wQ6tMjIjIUmdbla2LXwcouKcG/vdRfSnB765xv8Nm5F9XbER8I5RUVyC4rxdb8PMjmeRmzK1+btArzxqQBEWYLiuG9h2mM1W7IAFa0T0hCvxbpWFFncpm8ebl36Agk1tmoJ6/TJX36YVSnLqok5JuLLlMdCqTcYki79hjUqk31aGJ5TSRwveC0AThr7JGwmE0wm8P6gi41cYYJYoW01xo/fjwGDBiAQYMGqRZbJSUl1d0KiIgoeKQhvnQA8EZqTKWFU6CD2CKHA7M3rMc9C76vLmGQaWN3DR6KM7v39FrjK23Szu99BF7wkbE8v1dvGJWUhkh3hrf/XI53//pTfX+OSm+Ju08YXquEpCbp+1uza0FXH/erIsGs3Wao8IHClKF+Cs8//3xkZ2fj3nvvVZu5+vXrh9mzZ9fb7EVERIHnq99sFekvK9lRadMVG6DSLpkydee872odk3G5Dyyaj15pLVTf27pkdOpFvfti7sYNWJe7t9bapX36ISM+AUYlda8SZE7odxQuP7I/nC43omzWA3ZrIDIiQwWxQkoHWD5ARBR66bGxane7w127/ZKQ3qyb8/Nw9pxv1ZSv+4eNQKvYOL8HbK8vX+pzXWpDnxs7DtFeujFIScGtxx6vNjVJXW+U1YKRHTtjR2GByl7KsIeaG8FkgIBsfDrU7g2B5vZ4VCus15YtxYKtm1UHiiv69cfwDh0YwFLYMlwQS0RETUNqZBTuHjIU9yyYV+u4VJPedtxgtcFLcrXfb96IzOIiTDvtLDXG1F8cLhe2FhT4XN9RVKDG63oLYmUD2k2zv0b7+AT0TW+pShHumjdXTWWTWt87jz8BOnRsyc/HK8t+wx+ZmUiPicF1A45G7xYtmlxgKG8YzvzoPZRUVFQfm/LDHJzQrj2eOGms15G7hUVlKCpxwGoxIy3FuL1xqfliEEtERIckwmrFqd26o0tyCp7/fbHaDNQlORkX9OqDL9etwYqszOr7rsreo8ab+jOIlY1bR6a3xMo6m5iq9ElL99r/VILfz/5Zpf6+pSBf3Wr6dsN6/Kv/QGwrKMBFn3+sOjiIrQX56ut4fOQYdE9JVcdi7baATDXbXVysMquSGZbRxzKQIC3ae32xZI7/98tPtQLYKjJKVkouagax0uN1R1Ye3v50MVaty0RSYjQuGDcAfXq2RguOgyUDYRBLRESHLM4eoVppvZByqro0P/3PP3DbnG9VRrOujbm56NPCe0vEQ2E1m1UN6wd/r6zXl9asaSoQjfQSxErjAW/Hq9jNZlXPO3nenOoAVrSOjcNDI07C2yuXY8GWzaom+Ii0Fnhg2InomZoKm9k//6RKn93xMz9TY4CrdE5KwhunnoW28fFeN7dJttuXr9evrVUbvHbTbkx64BO43R60SIlFVIQNb322GH1WtcKVFxyP5MTAd5Qg8gf2xiAiosMmG7esZhM+XvWX1wBWtIz1f3DUJi5e9bxtV2MzlgSb0qfW1wYtCTZlxKwvFx/RF5quq2CypnuHDscd38/GD5s3VW9qk+EI5336ITbl5fnl65FShn99NatWACs25OaqNwfS29cbi8n7oAIRuW/MrMjKLsRTr89DSlIM/jNxFCYc3w1di0pxaqc0DO7VFsXF5X75OoiCgZlYIiLyC2mMf3q3Hvh036X6muRydrv4xOrWXNsLClSgJj1dW8bGqdZQh0I2W0mW8aNzzldDDCS0TLBHHLC1l7SbOr1bd8xau6bWccmsntK1G0rrXJrvkZKKTbm5Kttcl2Rrn178C54YNdbnSN+DlVNaWi94rrI0cydyS8vq1ePKJrSzevRUGWlvxnXtXv33klIH9uaV4L6rRuCpC59Gzs79z2WPtOGBr6agTatENVyIqKljEEtERH4hl+hlx79s4vp5+7bq4y2iYzD99LNUL1IpKRg/81NVH1tFxt2+ftqZKqt6qKRe1FfNqDfS5P/uIcNxYe8+eHflnyh3u3Bez944Ii1dBcDS47ZXapqqgRXdUlKwNHOXz8dbsnMHip2Oww5iC53lBxzzW1eExYKJAwZh4dbNqj9vTVf0O6pWVwiTScNZI3rjvcnv1QpghaPMiYfO+R9eXv440jIa7hVL1BQwiCUiIr9Jj4nFM6NPwZ7SErUxKjkyUk18kuN7Sopx1ZczagWwQnq1Tv5+Dp4/+VQkBLGFVdXUsqNatoau66rGtops1np05GhVKiBZ2SKHU30tviRFRsKsmfySzfZFhjjE+3h95A3Ax+dcgJ+2bVU1sDJK+LI+/dAxManWaxoTZUffji3w4S9rvT5OUV4xsrbsYRBLhsAgloiI/CopKkrdqnbwV8kuLVE7/L35Zcc2lf2sCrjkEr0EvbtLilXDfsniSu/ZqMPMdHpjMZl8lhx8feFl+HT136rF1oR+R+Lj1X97ve+/jhqIVD90XpCg+uTOXfHNhnX11i46om+DQa5kXM/rdQRO7dpdfU01g/IqqcmxyI30valNFOfXL5moS4L+vbvy4Cx3wmKzILllIswWliBQcDGIJSKioJCa1YZU1aFKp4Flu3bi+m+/rP4cCcquG3g0zujWQ9WMSl2oBLZSqiATqgJBxrG2S0jApGOOU+cmgfX9Q0fggYU/qNrbKid36YoRHTr65Tnj7RFqA5lkdiVgltdCNmZd3q8/xvc9ssGuClUOdJ+E1HjEJsaorKs3bbu1avDzC/YWYsmXyzDtng9VSUJMQjTOvmUcTr56JJLSjTvtjIyHQSwREQVFenRsgxu0JIATu4oKMWHWZ2p8bBUJIJ9d8qsasDD9z+XYmJerNoPJAAXJ+AYqkK0KZqvG5p7doxeGZLTDrzu2qylex2e0Q8uYGCT6sVes1PbeNWQYrj5qIMpcFaofrnyt/mrhldI6EVc+cjGevuaVemsjLz0BiWm+a5MrKlz4/u1FePm2t2plbt+67yPs3JCJ65+5QgW1RMHAFltERCEaE1rqdMJVp79pOJMOBSd17OR1Teo3qxryf71uba0AtiaZAiaBpJBOAZfMqL1JLNBk+leHxCR1af/q/gPRMzXNrwFszc1a0hO2a3KKqnf1VwArpPPAkLOPxj0f34qWHVuoY3HJsbjqkYtx9WOXIqaBPrG5u/Lw9v0fe137/p1FyN/te4Iakb8xE0tEFEQVbrdqLfXJ6r/VRKvOScm45Ii+KlA5mEvFRiabkv47fKSq65Q2XHKpXLKMsoP+sn2XyqXWcnVOts/H2FZYUKv2VPqmbti7V/WGpYMXlxSLE845Fr2P7w5neQXMVjOSWiYcsLVWUW4xSou896oVuzbtRpsDlCMQ+QuDWCKiIFq5JwsXf/5J9YQpuSz93l9/4qVTTsOwdh28bsYJJ3Kp/P+GDMM1/QepS+UyFlaOVX3dUhYgfV+/9bKxqaodl4xirUnGsx4Mufy/t6xU9aiV8gXZCCWX6cP9NW9IUsvK3r0HS3rJNiQmwf9ZaSJfWE5ARHSIdN0N3Z1TedO9X/6uSXbb3/LdN/VGpMr0J5nG5K2RfjiSjGvVpfLWcfH1gsiRHTr57Lc6od9RKotdt4vAgeSVleGN5csw8p1puHTmpzj/s49w8vtv4eftW+FwuQ7zK2o+4lPj0PPYrl7XEtLikdqWrbkoeBjEEhEdAt29C3rxK9BzL4Keewn00mnQ3ZkNfk5uWRl2FNYeJ1ql2OlUQwJIWkXF4oOzzlPTvKrE2e34vyFDsSIrq1ZDf7mPr/GyNS3L3Imnl/yiNohVKXI6cfWXM31+T6g+qZ29460b6gWrUbGRePDLyUhu1bjMLtHhYDkBEdGhBLC5lwDuHfsPFj0KvfRTIGkaNHO618+TjGtDXJ7ms8nrQN0AeqW1wIdnn68Cfwk8JTO7PHMnnlr8S/X9jmubgUdOHHXAEbPSf/bpJb96XXPrOmauWY3bjhvs968jXLXu3BLP/Pwgtvy9DWuXblQtuboN7IK0jGSYfPTcJQoEBrFERI0gZQN62de1A9gq7o2A4ycg6hyvnyu9TWXq096y+htjpEazdeyhj10NR7KBq+YmLsnQDmjdBoUOByItVtVL1dcEq5qkfKNuHW0V6Yjg2bfhrjnXxjZWaptkdRs45kiv66XFZdWdCuR+Vnt4b1qk0GAQS0TUGHo+UD7L93LZZ0DEaGim+j1RJWP40IiTMPHrL2o1yxeTjz+husUUeSdBpnRxaCwJeKVu9vddO6uPSQB81+ChKju+dm8OPl71FwZntFffI2lvRYdux7pd+Oypr/DTjN/UFK8RFw3GuGtOQqtO3q9QEB0qTZd+Js1EYWEh4uPjUVBQgLg4tmMhosbT3XnQ8y4HXKu938F2NLSEl6GZon3ukJeJU88s+RX/5GSjbVwcbj76ONVv9GCyinRolu7aifM+/VD9XYLUF08+DQ/+uEB9L6rIVDDpEjG4bTvYGcgekh3rMnHLCfcgf0/tzHfrzul4+Nv/YyBLfo3XWLxCRNQYpjggdgoQdTlgblNvWYu6xGcAW9Us/4gW6XhqzMmYcd5FePXUM3Bs2wwGsAEmmdjnxoxTGVgZXSsdDmoGsEJqb6//5stm0yXC3xzlTnz96px6AazYuSELS+f8GZLzovDFt5pERAdJd+2AXjYDcHwPaDHQoq+Wg9CLHgas/aBFXwOYkqBXrFJ/wpQGTfNeZxlrs6sbBYeMjR3TuQuOatkKxU4HTvngHZ/1s8uzdqkWYP4mpQsyUvf3nTuwcs9u9ElrgYGt26h+taYAjs0NlrysfPz6xVKf64s++RUnnHssElJ4JZT8g0EsEdFB0F1boe89D9Dz9h+r+B2wjwISXoeml0IvehJwb5CcHqAlQot/DLptEDRTZP3H8zj2PZamAl5N48aXYHQ9aBkbi815rlqttuqSgQiBsCYnGxd+9jGKnI7qY7E2G94/63z0SkuD0WkmDbYGhiHIoASrhZvnyH9YTkBEdAC6pwx68fO1AtgqbnchMh2d8FNWHL7KuR3rtfeRb70D0Iuh518DuLfXfzzXduhFU6HnnAZ97xnQi59VbbsoOKJtVrRroLfsgJat/f6cMuhCNvTVDGCretVO/GaWWje6FhmpGHvViT7XT/nXSYhO8F1qQ9RYDGKJiA6qI8E39Q5XmAdgedkUjP3gE4z/8idMmvs7xn40D1N+jUeO/bnKTy2dDl131i5JyD0XKHu/8nE9e4ESGZpw2QGHJZB/yJjb+4YO97p2bJu2qpWXv+WUlvocjyvDFmQ9HBw7bgB6eJnoddzpA9GpX4eQnBOFL5YTEBEdoj2mWzH+izlwuGuPLZ27aRu6J8fjhq5DYa5YA3hKAbMNul4BvewDwFN7Q5Hi3gY4fvbZY5b8S7Ktb59xDh5cNB/rcveqYQqX9umHy/ocieQo/2cL644abux6ILgqXMjPLoT0e4tLjoEtwncpwMFK75CGu967GRtXbMH37y6CxWrG6MtHIKN7a6RlcCQt+ReDWCKiA9ESgIhTgPKZ+4+ZO+K3rJJ6AWyV6X+uw/ldz0W6dS6g7auJ9UhG9zufT6NL/9mIsQ12Nwh3ugT8kp3WC6XVA2BKhiYdIRrB4XKpnrINbZaKsdsxOKMd3j3rXJS7XDBrJtWnN1ADD5KjotRAC2/BqhxPjgxuj+A923Lw5Stz8N2b81UwKxuuzrv9dLTq1OKwHzu9fZq6DTr5SGiaBouVoQYFBn+yiIgOQG3MirkBunPR/iyqKRHbC8t9fo7UPrr0SGhRl0Mz7etCIJ0KqgJar08ka81344vuzoZe/BxQ9mnl5jhhGwLE/xeaudUBd/7LVK7ZG9Zj8Y7t6JCQiAuO6IPWsXGIsvreNJcSgKyrN6lR0bh+4NG1xuZWuW7A0UEddJG9PQd3jHxAtb2q8vUrc/HTZ0vw3OKH0bLj4QeywmrjZkUKLNbEEhEdBM2SAS3pUyD6ZsDSU2UIj2rZ3uf9M+LjEWFrCZgz9j+GdCGIusT3c0SNh2Zqnv1idU859JKXgTIZSFAju+38EXreTdDdew+483/cB+/gkZ8XYcHWzZj25x8Y8+50LNyyCU4f2fJgkgELFx/RF0+cNFYF1kJqb584aQwu6dMXkQ0E2v72x/d/1QpgqxTkFGLWC7NRWlh/LDJRU8RMLBHRQdIsbYCYa4Goi+UDdLXqaBsX73XDzuTjBiM1NgOaVufXrH0YYB0ISHuumqRcwdIdzZYnGyitnKhVj2sl4NkDmJO9Lu8tLcXtc2ej2Ll/A52QcZS3zZ2NOZdMOKRxtf6WFBmFs3r0xOCMDFS4PbCaTWqTWTCVFZdj3nuLfK7/PPM31WGgXVz9QR5ETQ0zsUREjSDDCzRzAjRTDNJjYvHumediaLv20u1VSYmMUtm1Y9u2rx/AqoqCFtASnoKW+CYQcTIQcTq0pPehxd4NzUeQ1izo0mKqwvd6A50b8srLVCbWG6l33ZxXvzVaKEng2jouLugBrDBZTIiI9p3tl7Vtq3egoiL02WuiA2EmlojoMMhkp2fGnILcsjK1aUemcLWIiWlwU5FmTgPkZjuu8mON+QS1iUvlVXwMITD5Hgbg9kjOtWnt/G+q7BE2nHb9aPz6pffJWiMuPB5Lv1uB/if1gZUbsqiJ429OIqLDFGePQPuERHRNTlEToQ52hKgErwxg9zGlAPax3tfMHSqDfh/iIyKQHuM9qynfi85JSf46y7DQvndbnHDuMfWO9zqum+oqEJsUA3s0RyJT08e3WUREFHKqrVjcndALCtVmrmrmTtASX67MXvvQIjoaD484CVd+MUPVwdZ0bf+BQW9f1ZAKtxt7SktQUF6uWmtJnWxSZAMdKwIgpVUyxj9wAQafeQwWf7UUrgo3Bo05Em63G8/d+Cae+/UhmAPUaozInxjEEhFRk6CZ04GE/wHSiUA2cpkSVYZWMzfcJF96kQ5q1QYzzr8YT/76E/7O3oNWMbG4cdCxGNCqteoJ2xTkl5fh63Vr8dgvP6pxs6J3ahqeGn0KOgU5W5yWkYzsbTmwR9rgdpVj2j0fwhZhxYNf3okW7VODei5Eh0rTdb3hYqIwUlhYiPj4eBQUFCAurnHNs4mIQkn3FMgM28oqMAnspOcs1VPkcKCkogJ2sxmJQc5wHsi369fh+m+/rHdcNgPOvOBitNrXeitY5J//nF25KNpbBM1kQnxKLJLSE4N6DkSHE68xE0tE1MT7p8K9HnrhY0DFUsAUD0SNByLPbvASe/Xnu3dXTgqTC+0qs5mmMpfhKtZuV7emJrukBI//UqNMooacslL8mZUV9CBWfg5SWyerG5ERMYglImrKXP9Az71I9uBXfiwTw4qfgu74BZBWXT4uteu6A3Auh15wJ+DZ157K1AJa/MPQrQMqp5A1QdJJIKu4CD9u3YJtBfk4uk1b9EhJUxvmjEwGLmwpkDcT3v2RuQtju3QN6jkRGR2DWCIKe7ruBDwlgBbRZIM3b3RPLvTC/+4PYGuqWAK4dwC+6kXdO6DnXVF7+pVnN/S8q6ElzwJM3dDUOF0u/LZrh9qgVeGpbLX12vJlasLVe2edi4z4BBiVxWRSo2VzSqUkpL4uycyGEjUWe7sQUVgHr7prgwoE9bzx0Atug+5cAd1TCEOQwNv1t89l3fGT76+75O3aAez+B4Ve8lplmUITI7v2//XVrOoAtsrOokLcv/AHVe9qVKnRMbhuwNE+R9Ie22b/eGIiOjjMxBJRSOju/MrMoONH6a8EzTZE9QLVpObTX5x/Qs+bsH8SlGsNdMf30OIegB5xhgGysrJ5y+pzkpXP18pTBrhW+X7YilWAXiLhE5qSdXtz1IQtbxZu2awGSjTFeteDIf1qx3Xtjo15uXj/rz+rW4ElRkTi1VNPN3y5BFEoMIgloqDT3XuhFz8LlH2w/xgeAaKuAGKugSYbkA77OfZAL5zsNQDUCx+EZhsMmNqiSTMlAZGnAWWfeVnUAPsQ75+nRQDm9kDFSu/rlnaV9wkBXbLLepEK0DVz7VZO+eW+s8MS9FV4jD15S8oJ7jhuCK48sr+q942x2dWQhhbRMTCbeGGUqLEYxBJR8FX8VSuArVb6JmAfDti9X3ZtFNmR797u6wQA92bA0rSDWM0UAUTfAN25FHBvrb0W9yBg8t7PUzPZgejLoZd/4X09+trK4QLBrkt2ba588+L8rTJAj74SsI+o3pzWK9V3twUJ9mJsNoRL9wSZ8EZEh4dv/YgoqKQeVWoyfa6XvAHd433zi38Zo82UZmkNLekdaAkvARHnqKBWS/4GiBjbcCBqbgct/n+AVrNkwl4Z/Fo6IeiklGPvWYBjLqAXqDcReuHd0AsfUBvYRFp0DEZ16uz10+8ZMkxlLImIqjATS0TBpVcAel4D67mAZO1wmKNCTQmAua2PbKy18nK7kSZZmdOhRZx48J9jioEeMRqa9SjAs0tSoYC5lcreqkxt0Lss/Md7ba/jO8A9UWVmZTjBf4adiN6pLfDmimWqvKBLUjLuGjwUR7Vs6bf+tro7p7J7Q8VyaKYUwHpkZT22ZvxML1FzwiCWiILLFAfIJi7XBu/rtqGA6fA3uahBAHGP1N7YVbUWd7eaehXuVFBmaQ1AbiHkKfZdn6u6LPwMzdqzOhs7ccAgnNOzF1wej9q5nxLlv9IH3Z0FPf8moGJF5cfq/23QEl+GbhvEQJbIQFhOQERBpWlWaFEXSWGml8U4aJFn+G+kqq0vtJQvgMgLAEsPwD4KWtLHQMQ4A3QmCLd/anx/T7U6m8xkk1N6TCzaxMX7N4D1OKAXv1IdwO7nhJ53DSDTzYjIMBjEElHwmdtUBpO24/cd0FQGVkv+SK35i2TVNEsnlXnVEqdDi38cmq0fND9keqkRpNuEfaTvdUtH6O5dgT8PTw5Q9qmPxQqgYnngz4GI/IblBEQUdCrTau0CJDwDVA0eMMUHLLhUl4jNvEwcKmoDWuzt0CUD6qmd7dRibqzsomDpDkSNh6YFMrciPWgdDZYaGGO7HxEJBrFEFDKa1MfKjcKflgAt/mGgYg30ij9Udlazj6icOlY2A7AcAUScCZgDOFpWiwLMGYB7m/dl21GBe24i8jsGsUREFHiaBr34eUAvAyzdAE8e9Pxb9mdGpS5WC+w/SWq4Quxd0POvrb9o6anakhGRcbAmlqiZkYlJuisTujsTusf3hCQif2fdtajLVL9YlM8CHPNqXdrXoieotmABZxsILeHFyvZrihWIPBda4kv1JogRUdPGTCxRM6HrHsC9BXrRE4Djh8r//CNPBaKvh2bx32Yqan7DK+DZC7j3VLZGM6VUtjfzxjYIsJ0AOBfVOT4csPYLyvmquuuIkYC1D6CXApoV0JIrp6MRkaEwiCVqLtzboe89W0Zi7TvgBMo+q6xJTPoImqUVmirdvbfyfGH2HSBR0OnuPdALHwIc3+4/KJfkE19WXSHqUpnO+EcA13ro+7oEaJHnqQliVaNng4U/R0TGxyCWqBmQufV6ybQaAWwNslvcMR+6+SK/TUTyF91TADj/gF78hAp8YGoFxFwH2E+EZk4OwPMVA55swLkYul4OzXZM5aQsaRFFtUgpil7ySu0AVri3Qs+9HEj+uHLSWB0qWJWb7ejKjwPajYCIwhmDWKLmQAWDC30u647v1JABrwMIQkTXXUD5XOiFd+0/6NkFvfBuIGo9EHOzX2so5bK4XvY5UDS1eo6T+v+IU4HYKQeVKXS4XMguLUFOaSksJhOSI6PQIiYGpib25sBvPVdLP/KxlgW4tqk3AL4cavCqu3ZWvslw/lqZwY0YC5hlJG1wR+kSUegxiCVqDqQvqyatrHb6WI9ver8OPHugFz3ifa30HSDqEsCfG4HcO4Cih+sfL/8SsA0Gos5s8NMLyssxc+0/ePTnRSh3ST9SICUyCs+NHYcjW7aCzeynKWRNRvm+Eo8GXk8M8usz6q6N0PdeBOh5+48VPwct4SXo9uPUNDgiaj54HYeoGdBMSdCiL/e9Hn0ZNFMTy2R58gG90NeiqvH1F113Qy993/d66Wv76nJ9+3N3Fh5Y+EN1ACtyykoxftZn2Fnk6+swssiGM/eWDn59Nl1achVMrhXAVnJBL7ipcmMZETUrDGKJmgsZ8WobUf945GXqsmzTYztw43p/kdIFd+1JUrV4JHDaH5zWlVdWhqcW/+x1zel2Y9aafxB2ZGNUlI83RuaOfh0fXP09qPjT+5r0nnVv8e/zEVGT18SuHxJRoFTuDH+osktB+WwZT1RZT2hqCc0UwClJh0o2U1l6AC4vAaCWoOog/UVloWV6lK+6YetAmY/q8/Mdbhc259fNEO63KnsPKtxuWMOopEBduo+6CLq0qZLyDlRULlj7Q4t/3P89V+WNRoPrXjYtElFYM0wQ+9BDD+Hrr7/GihUrYLPZkJ+fH+pTIjIctaPfnAzNFpyenId9rglPQN97SZ1LyDZoCS8AJj+3SLKfAJSkVG5YqsUKLeYGaCbfl84jLBZ0TkzCH1mZXtf7tkg3VACr6xWAJ7dya5sW43MDndrsFjMJiLoY0Asqs+NSulLnTZHultKQ3Mp+slKbrXrJNrK7hBpRnKZqpb2ydGnc4xGR4RmmnMDpdOLcc8/FxIkTQ30qRBQs5s7Qkj+HFvcYEHk+tNh7oKV8A9j6QZPNan6kWVpDS/pAte+q/tVo7QMt+QPA0r7Bz02IiMQtxxzvdc1utuDUrt1hFLo7S22W0nPGQc8+EXr+7dAr1lUGtl7IkADN0haatTc0S0cvAexu6AV3Qs8ZAz33Yuh7T4Wedzl06V7QGKYW6vvvVeRFgMn/LdeIqGnTdF2v7CVjENOnT8ekSZMOKRNbWFiI+Ph4FBQUIC5OdmoTEXnrFSu/XzxqAtXB9oiV7gTfbliHh35cgJKKyoAvPSYGz44ZZ5hMrBpekHdV5WjYWuyVbyasXRo/4rjgXsDxZf1FS2doiW81quxAfW8qVkEvfhyo+Keyh2/0RMA+PCB9g4koNA42XjNMOcGhcDgc6lbzRSGi0NDdOZWDFdy7VB0uzC2COqtejUeVLKPUA+vF0OwjAckc1un/qi6dH0LrrviICJzdoxeGZLTH3rJiWDQNSVFRaBEd1+SGSPhUsdpLACsc0IufBKTWtTGvjZQPOL72vubaUFka0IifAfXc9qMB62uALi2+LEH9GSKipiWsg9ipU6figQceCPVpEDV7umsH9PzragdI5k5A4ivQLBmBf35PAfSSd4GSZ/YfK50OWAcBCU/6bQSpBfloZV2Mls5XK2tKKwYBronQLRnQtAN0W2gC9PI607dqcvyogn+gEUGsbPqSjLYv7mwpOW40TlAjopDXxE6ePFllKBq6rVnjLStwcKZMmaJS0VW37dv911eSiBrT3/P2+hk+90bo+TcdsP+qX7h31gpgq1X8Br38K+h6A4HWQZLNS3rR49ALbqnsqKDG+X4Jfe/pPrKbh/maurZU3lT7Lz9pqEuF6gnbyIyy+pwGolRzi8Y9HhFRU8nE3nbbbZgwYUKD9+nYseMhP77dblc3IgohuaRc8Yf3NdfqyoxlgOsZ9dKPfS+WvAVEjKvse3o4JGgt/9zLQgX0ggeApNcPO4OoRvG6NkAvvGd/z1RrHyDuv2p3vqYd3q90GT2sl07zvhh1CJunTClA5NlA2Yf11yx9qjtMlBSWwlHqRES0HfYoG/L3FEL36IhNioY9kr/DiagJBrGpqanqRkRhzCOXlBugLlEHjsqy1mubVfMORQ1f8j7Y53Eu8b3o+guQmtzDvQzu3gk99/zK5v5VKlZCz70AWvKsA3ZROCBzayD65vpZa0tPaJFnNzpI1kyRQMyN0GVQRNkM+QIqF2zHQoufipKiCGz++x+8999PkZdVgOuemYCVi/7Bt6/PQ3mpA8edPhAXTj4TLTu1gMlkmGY6RBQkhqmJ3bZtG3Jzc9Wfbrdb9YsVnTt3RkyMH+enE5F/meL3VS75CBQDPGhB00xAxCnQHXO838E2GNBi/fA8Numq6mv1sKu3pMWVXvpB7QC2erEMeul7QOwdlUMIDpFmioMeMQqatQd054+ApwSa7ejKp5CSkIRnG10/rDZexf4fEH1N5RhhKTHQkuGsiMTCjxfi6WtfVfe7860b8dItb2Hjn/snb303bT5+/GwJXvz9EbTu4r/hFkQUHgzz1vbee+/FkUceifvuuw/FxcXq73JbunRpqE+NiBoil6AjTve+Zh8TnP6etiMBs7cspR1a7E0NDjI4+Oc4xnfNqG3I4Qfr0l7K+avvdckEy30Og+4uAArvU7XKcG1RWWq9+GnohVMqS0Jc6w7pceX11Szt9g0rKFU1w3mZe/DSLdPVenr7NJSXOWoFsFVKC0vx4aMz4Sjb32mGiMhQQaz0h5WWtnVvw4YNC/WpEdEB2iJpsbcBkRfW2ORjASLPgRZ3t8r+BfwcpJ9o0nQg8hKZr1UZbNpOgJb8GWBu558nkfrP2Du9PHkitLi7oJkOM9ur2RueUmZKVaOED08pUCGJASfg/BlwzKus9d1Hd8w/5EdWAxNyL4Cee5YadpCzYwMcZU611uOYLlg2Z1+Nrxc/z/gNRXsDW3ZCRMZjmHICIjIudQk6djIQfVXljHu5pGxKhmaKCuI5tALiJgMx/6osbVDjVP0XQKseppHnqLZd6tK+JwuwD1P9aGUa2OE/gQ1a9ATozoXel6Ov9kNGWVOvi8865UPMmuuuXdBzL60xPlivVeNa4XQhIsr3Bi7Z7KWZDNJrl4iCxjCZWCIyNtnkUzmetHvln0EMYKvPQQJBycqaWwUkAyyPqdl6Q4v/L7SEF6FFXeafALZqU5fzDyDq8vpr0dcCVj+MtpUgVboQ+KBFjD60x3WtrxHACg+SW1QgKq7yZ2D5vL9wzLj+Pj993DWjkNBCaquJiPZjJpaISG2ccldOkJK6UnXpPvmQM5tqF/9htruqfW4e6GVfACXPAZEXQEt8HaiQ3rO6Cl71is3SWuCwn0dtCou6BLrj18qOCjXX4h4ETIfW11V316l1NbVCSnoB3t90G7K352HDn7nI21OAUeOHYc5bC2rdtVPf9hg1YRjMBhjbS0TBpelSWNpMHOwsXiJqXnR3HuCYDb3o6X0ZQxMgZQBSyyplCKE+P0859PyJlXWqiml/La97a2XAnfw5ND8ND9BlkpZrE3THAsCUBC3iRBXAHmpQL0Gxnje+8gNrP2jR10IvfqZyKISwdEFhxWTs3JQIj8eM2dN+QGlhGU66dCi6DuiElNZJfvm6iCi84jVmYomoWVN9ZB3zoBfeV+OoHJsDPW8rkPhmZZuoUJIMqaVTjSDWA7gl+7qPuY3qtOC3p5Ov15wKzV7ZXuuwWTpW9qB1Z0GLuQl6/vW1W4W51iNOuxbxg76AZu2EXsd3U0lmk5kVb0TkG39DEFHz5tkDvfhJ72uutYA79OOqNc0MLfICn7+ytZgboZkD22/3cEiGWEucDkRdBr38e++9bmWyWek06B6H2vTFAJaIDoS/JYioeZOAqqGJXhWr0SSYW0NLeL6ys0M1KxDzb8DSG02d6hMbfQXg+tv3nZzLAj7BjYjCB8sJiKh5U71V5VK8j2b6TaAmtqq7g24fCi3lK8C9SxqvAua2QW9Vdjg0LR66SSZvrfR+B3N65aY6IqKDwEwsETVvWjIQebaPtRjA2g1NhXQP0CQjaxsIzX5cyFqVHSrNFAEt+krf69HXVPbbJSI6CAxiiahZU4FVzETAekydhThoidMAU3qoTi08WTpCi/2/Oi3BNCD6ZsDih163RNRssJyAiJo91Zoq4enKEavSmF+a/ps7ALIhSWN/Un8PhNBl5LB9OFCxqrLTgrX3vrIIZmGJ6OAxiCUiUoFsEiA3a49Qn0rYU/1m5WbJCPWpEJGBsZyAiIiIiAyHmVgiIgoo3VMIePZWtjLTYitLB0I9QIKIDI9BLBERBYzu3gO98CHA8e3+g+ZOQOKL0CwdQnlqRGRwLCcgIqKA0D3l0Eterh3ACvdG6HlXQHfvDtWpEVEYYCaWiMKO7imtvHStF1VOuDIlVU63kkvacAFaFDRzWqhPM/zJ96D0Y+9r7p2Ae4fqAEFEdCgYxBJRWNHd2dCLnwPKPt0XsCZAS3wJeumHQPk3MkcWMGcA0qtUhgawrVNgR/rC6Xtdglj0D+YZEVEYYTkBEYUN3VO2L4D9sDKAlXZOsXdAL7wHKJ9VGcAK9zbo+dcAFcvRVOjuHOgVa6A7lkB3bYLuzofhaVGAFul73dwumGdDRGGGmVgiCq/L1yoDu4+WCGhWwLXB6931woeBpHegmVOCd47ezsO1FXr+xNrnaRsOxP+nchBDE6B7SirLMdy7KoNT6S5gkmEQDeRC5D6R44HSl72sdQLMrQJ6zkQU3hjEElH4kBrYfRlYxdIResXfvu/v3rjvkneId+/n/Qtwb6694JwPveh/QNx9lcMB/PFcuhNwZwGOhdDdm6HZBgHWvtDMLQ9wjnuhl7wBlE6TF63yoLTJSngRuvUIaJr3f0o0zQZEXwodpUDp+/u/N9b+0OIfY10yER0WBrFEFD5kExc0CbsqP9YLoZmSqz7ycbk7xL8GZYd+3QC2SvmXQMwNldOtDpOuVwDO3ysD5n1lFXrpu4ApDUh6F5qlve9PdvwAlL5e+5hnL/Tc8dBSvgYsbX1+quoHG3MrEHUZoBdUvuamJGimxMP+moioeWNNLBGFD+lCYB+5/2PXesDS3ff79cgLAFNoSwngaajNlFsiTf88j2R886/fXxdc/fx7oBfcA91d4DtTXPy8jwctBxw/HfCpNVMUNEsGNMnaWjoxgCUiv2AQS0S16J6CysBF2lQZjGaKhRZ3N2A9pvqYXvo2tLiHK1ts1SSXtKMvhyY1s6Fkbt3Aog3Q/NQ9QbK9vgLiiiWAnufjE12AJ9Pnw+qutf45PyKiRmI5AREpuicPqPgLevFLgCcLsPQBYq5TO8g1UwSMQtV3Jj4DuHNUllGys7opFVrKbKBiBXTPXmjWo9SmolBv6FJMqYClN+DyUrsb5cdMsYx+bYjUy6qyA3fl66Y7AM0OHRGAuYPPkgfNdqR/zo+IqJEYxBKR2nmul7wLlDy3/6B7J3THHGiJ0wD7/symEajL1eqSdZfKj6sWLG33/72JUIF0wvPQC6cAzl/3HbUAkedAi77Gf28grN0aDqRNcZUbuMpmAiWvAHo+oMUB0VdBS3ga+t4z9tcaV598AmAd4J/zIyJqJAaxRFTZmqrkBS8LbuiFdwNJH1Ru0AkTumQl3VnQy+equk7NfiJgbgvNnByS89EsrYD4Zyov6esllcGjKUXVkvrvSVKAiNP39cutsxR7F3QkACXP1t7ApRcCxU9WjodNeB0ouKny/ISlC7T4p9gmi4hChkEsEQGqrtHjfc29DfAUVPb8DKHKy9y5lR+YEg65llWGCOjSKqrkpf3HJPOo+rI+6JdgXXUCUGNvKwAt4qBaSWnmBEACyQDRzPGADH6w9gJKXq08P0s3NQwC1n7QPNnQS9/y/sllH0KLHg+kfAV4ZAiDTWW6m0Q5BhE1WwxiiejAvwoaamgfBLo7s/Iytxpk4AEiTgMiz4NmaWhTlA/uLbUC2GrO+YBjERB19mGe6x7oJW8BZe9XZi3NbYCYfwP246CZ4hFKKkCXVlcRY/f1e7VXZ5911+b6nQuqyRuIvMr61wY3ohERBQ+7ExCRujRcb/d+9Vr3ytrHENHlsn/u5UDxU4B7u6rVlSBUz70QukyPasxj6a7K3qi+1kvfUHWhh3yunlzoBXcBpa/tv+zu3gG94GbAMR+67iPbHUQyYUumgGlqY1uN8gntALW3DY2PJSIKAQaxRFRZfxn3n/rHtSho8VOhmZMQMtKH1L2p/nFPlsrOqjKDg6W79pck+NzBX2PiV2O5swHnIu9PXfRY5a7/psqUDFi6el8zt6tcJyJqQhjEEhE0UyQQMRpa8iwg4mxAxpFG3wAt+Yt9wwJCQ/cUQy/73Pcdyr7cV6N5cGSnvxYx2vcd7EMA7TAu+Ves9r2mamRlLG7TJFlZLeFZwNSi9oKMl018Maw29hFReGBNLFEzJZfp4dpa2RPW3BEwt4Rm7QHE/6eyZ6hsSNLMIT5LkzQi9b2s1hrZNEsCVVN65ddd67EioUX/6/BaWjXY3UByBnY0ZZqlI5D8MeDaAN21Dpq5M2DtWtl7l4ioiWEQS9QM6RXroOddUfvytqUnoDJurYBQT7HaR7WYiroEuvMX7+tRFze61EF9fUnvQZeWYpLJlfIB2xBosXeqNluHxdIJ0KL318PWZB9eORa3iVMBq7yhkWCfiKgJYzkBUTMjPT/1vKvq12e6VkMv+A90TxO75G3tC9hO8HK8H2AfekgPqcnQg9j7oKXMhZY6H1rCk9CsXaBph/m+3tQCWuJrgEy5qsncHlrs/0Ez+WmELBERMRNL1Oy4M+tfSq8io0+lxtQUi6ZC1WLGTwVca6CXvq/aPWmR5wHWI9Qu+8OqA5abP89Vs0CXnqsp3wAVf0B37YBm66cytIdzrkREVB+DWKLmxuOlhZR9GLTICyuD24pl0OUSu2zoMcWhyQSycrMdLX2yoJmabm2pyuZa2qhbUxtxS0QUThjEEjU3des+I06FZu0LPf+G2s3uo8YD0RND216rDk02cjEyJCIi1sQSNUMyKtQ2eN8HVmgR46AXPVR/WpOMIK1YEoozJCIiOiAGsUTNjGZKUgMMEHmO2hilO36Q7V5e76sXvwTd3cBwACIiohBhOQFRM6Q2GcXeA10mVBXd4/uO7t0AnME8NSIiooPCIJaomZLd+XLz2I4DHAsqD8po0chzoVl7qeys7tpd2feUiIioiWE5AVETobv3QHdtgu7aDt3jpVl+gGj2kYAWB1j7Q4t7GHAuhZ5/I/T8W4CKpYCH5QRERNT0MBNLFGK6pxhwLoFe9CDg3ln53tI+Aoi9C5q0ago0c2toSR+q1lt63pU1ygfcgOM76BV/AEkfQ7O0Dvy5EBERHSRmYolCreJP6PkT9wWwwgM4voeeNx6628dQAj/SNE0FsnrZp97rXz3Z+8sNiIiImggGsUQhpLv3Qi96xPuieztQsSZIJ1IIOBf7XnbMg+4pD865EBERHQQGsUQhVQ641vpc1Z3B6tNqAUwJvpdNKYBMoiIiImoiGMQShZQZ0OJ9rmrm4NShauZkaNFX+l6PuqRynCoREVETwSCWKJRMqUDUBB+LFsA+JHjnIlO8IsbWPx4zCbC0C955EBERHQSmVohCSNPMQNS50Cv+BJw1N0/ZoCW8AJjTg3cuMo427j4g+l/QHYuk9xY0+1DAlAbNFBu08yAiIjoYDGKJQkwzpwEJjwLSiaBiZWVtqqUXYE6DptmCey6mJEDG0qphB0RERE0Xg1iiJkAzJQJys/YI9akQEREZAmtiiYiIiMhwGMQSERERkeEwiCUiIiIiw2EQS0RERESGwyCWiIiIiAyHQSwRERERGQ6DWCIiIiIyHAaxRERERGQ4hghit2zZgiuvvBIdOnRAZGQkOnXqhPvuuw9OpzPUp0ZEREREIWCIiV1r1qyBx+PBK6+8gs6dO+Pvv//G1VdfjZKSEjzxxBOhPj2isKS7M4GKv6A7FwPmdtDswwBzOjTNHupTIyIigqbrug4Devzxx/HSSy9h06ZNB/05hYWFiI+PR0FBAeLi4gJ6fkRGpru2Qc+9BPBk1ThqgZbwEmA/FppmC+HZERFRODvYeM0QmVhv5AtLSkpq8D4Oh0Pdar4oRM2F7s4HdPmZ1wBTPDTTwb1x0z2F0AsfqBPAChf0/BugpXwLWNoE5JyJiIjCqia2rg0bNuC5557DNddc0+D9pk6dqiL5qlvbtm2Ddo5EoaLrLugVa6DnXwc9ZyT0nBOh598C3bURB3XhxZMHOH/ysegAXOv8fcpERETGCmInT54MTdMavEk9bE07d+7EmDFjcO6556q62IZMmTJFZWyrbtu3bw/wV0TUBLh3QM89H6hYuv+Y80foe89XawdWIaGw72VPgV9Ok4iI6HCEtJzgtttuw4QJExq8T8eOHav/vmvXLgwfPhzHHXccXn311QM+vt1uVzei5kLXndBLpgN6mZfFQuhlXwAx10LTzL4fRIsFTOleygn2sfby3wkTEREZMYhNTU1Vt4MhGVgJYPv3749p06bBZDJkJQRRYHkKAeevvtediwD9UkBroD7WlAYt7m5V/1qPfQxgTvPPuRIRER0GQ0SCEsAOGzYMGRkZqqVWdnY2srKy1I2IapCuAaZE3+smedPYcGcBKeOB7ThoidMBS499n5cExNwBLe4eaKYEP580ERFR4xmiO8HcuXPVZi65tWlTe1e0QTuEEQWE6kAQ/S/o+dd6X4+eAM0UcRCPEwPYjwMs0wCUAzADppSGyxCIiIiCyBCZWKmblWDV242I6rD2BSIvqn88+nrA3LlRD6WZk6CZW0Ezt2AAS0RETYohMrFEdPA0czIQMwmIuhi682dokkWVrKop9aB7xRIRETV1DGKJwpBmTgDMCdCsXUJ9KkRERM23nICIiIiIqCYGsURERERkOAxiiYiIiMhwGMQSERERkeEwiCUiIiIiw2EQS0RERESGwyCWiIiIiAyHQSwRERERGQ6DWCIiIiIyHAaxRERERGQ4DGKJiIiIyHAYxBIRERGR4TCIJSIiIiLDYRBLRERERIbDIJaIiIiIDMeCZkTXdfVnYWFhqE+FiIiIiLyoitOq4jZfmlUQW1RUpP5s27ZtqE+FiIiIiA4Qt8XHx/tc1/QDhblhxOPxYNeuXYiNjYWmaaE+nbB4pyRvCLZv3464uLhQn07Y4+sdXHy9g4evdXDx9Q4uvt6NJ6GpBLCtWrWCyeS78rVZZWLlhWjTpk2oTyPsyH+U/A8zePh6Bxdf7+Dhax1cfL2Di6934zSUga3CjV1EREREZDgMYomIiIjIcBjE0iGz2+2477771J8UeHy9g4uvd/DwtQ4uvt7Bxdc7cJrVxi4iIiIiCg/MxBIRERGR4TCIJSIiIiLDYRBLRERERIbDIJaIiIiIDIdBLPnFaaedhoyMDERERKBly5a49NJL1XQ08r8tW7bgyiuvRIcOHRAZGYlOnTqpna9OpzPUpxa2HnroIRx33HGIiopCQkJCqE8n7Lzwwgto3769+v1x9NFH47fffgv1KYWlRYsW4dRTT1VTkGRq5cyZM0N9SmFt6tSpGDhwoJoSmpaWhjPOOANr164N9WmFFQax5BfDhw/Hxx9/rP4D/eyzz7Bx40acc845oT6tsLRmzRo1QvmVV17BqlWr8NRTT+Hll1/GXXfdFepTC1vyBuHcc8/FxIkTQ30qYeejjz7Crbfeqt6I/fHHH+jbty9Gjx6NPXv2hPrUwk5JSYl6feVNAwXewoULcf3112Px4sWYO3cuKioqMGrUKPV9IP9giy0KiC+++EK963Q4HLBaraE+nbD3+OOP46WXXsKmTZtCfSphbfr06Zg0aRLy8/NDfSphQzKvkq16/vnn1cfyBk3mzN94442YPHlyqE8vbEkmdsaMGer3NAVHdna2yshKcHvCCSeE+nTCAjOx5He5ubl477331OVXBrDBUVBQgKSkpFCfBlGjM9zLli3DyJEjq4+ZTCb18a+//hrScyMKxO9pwd/V/sMglvzmzjvvRHR0NJKTk7Ft2zbMmjUr1KfULGzYsAHPPfccrrnmmlCfClGj5OTkwO12o0WLFrWOy8dZWVkhOy8if5MrDHIV5/jjj0fv3r1DfTphg0Es+SSX8uSSU0M3qc+s8u9//xvLly/HnDlzYDabcdlll4HVKoF7vcXOnTsxZswYVa959dVXh+zcm8vrTUR0KKQ29u+//8aHH34Y6lMJK5ZQnwA1XbfddhsmTJjQ4H06duxY/feUlBR169q1K3r06KHq2qSg/dhjjw3C2Ta/11u6P8iGOinbePXVV4Nwhs379Sb/k98X8oZ39+7dtY7Lx+np6SE7LyJ/uuGGG/DVV1+p7hBt2rQJ9emEFQax5FNqaqq6HeqlEyEbu8j/r7dkYCWA7d+/P6ZNm6bqCCl4P9/kHzabTf0Mz5s3r3qDkfzukI/lH34iI5MrkbJBUTbQLViwQLVFJP9iEEuHbcmSJfj9998xePBgJCYmqvZa99xzj+pfyiys/0kAO2zYMLRr1w5PPPGE2vFahdmrwJAab9mwKH9KDeeKFSvU8c6dOyMmJibUp2do0l5r/PjxGDBgAAYNGoSnn35atSC6/PLLQ31qYae4uFjV0FfZvHmz+lmWjUbS55v8X0Lw/vvvq/0h0iu2qs47Pj5e9fgmP5AWW0SHY+XKlfrw4cP1pKQk3W636+3bt9evvfZafceOHaE+tbA0bdo0KTT2eqPAGD9+vNfXe/78+aE+tbDw3HPP6RkZGbrNZtMHDRqkL168ONSnFJbk59Xbz7H8fJP/+fo9Lb/DyT/YJ5aIiIiIDIeFdERERERkOAxiiYiIiMhwGMQSERERkeEwiCUiIiIiw2EQS0RERESGwyCWiIiIiAyHQSwRERERGQ6DWCIiIiIyHAaxRERERGQ4DGKJiIJowoQJ0DRN3Ww2Gzp37oz//Oc/cLlcal2GKL766qs4+uijERMTg4SEBAwYMABPP/00SktLaz3Wjh071GP07t270efx0EMP4bjjjkNUVJR6DiIio2EQS0QUZGPGjEFmZibWr1+P2267Dffffz8ef/xxtXbppZdi0qRJOP300zF//nysWLEC99xzD2bNmoU5c+bUepzp06fjvPPOQ2FhIZYsWdKoc3A6nTj33HMxceJEv35tRETBounytp+IiIKWic3Pz8fMmTOrj40aNQpFRUW45ZZbcP7556s1CWJrkl/VEqzGx8dXfyxZ3BdffFEFu7m5uSqD21gSCEvQLOdERGQkzMQSEYVYZGSkyoy+99576NatW70AVkj5QVUAKyRwlfKCkSNH4pJLLsGHH36IkpKSIJ85EVHoMIglIgoRyaZ+//33+O677zBixAhVXiBB7MF44403cMEFF8BsNqua2I4dO+KTTz4J+DkTETUVDGKJiILsq6++Upu2IiIiMHbsWFVCIHWxB1vdJZf+P//8c5WBrSJ/l8CWiKi5sIT6BIiImpvhw4fjpZdeUp0FWrVqBYul8ldx165dsWbNmgN+/vvvv4/y8nLVwaCKBMAejwfr1q1Tj0NEFO6YiSUiCrLo6Gi1KSsjI6M6gBUXXXSRCkKlE0FdEqQWFBSov0vGVboaSOeCqtuff/6JIUOG4M033wzq10JEFCoMYomImghplyWlBRdeeCEefvhhLF26FFu3blXlB7KBq6rl1h9//IGrrrpK1cLWvMnnvfXWW9U9Zxuybds29Vjyp9vtrg6Gi4uLg/K1EhEdLrbYIiIKcYutmqQkQFplSUZ11apVKlPbpUsXXHbZZbj66qtxxx134IcfflBrdWVlZaF169aYMWMGTjvttAOehwS8dUmgPGzYsMP4ComIgoNBLBEREREZDssJiIiIiMhwGMQSEYUZqaeVFl7ebtLSi4goHLCcgIgozMgIWrn5mg4mdbNEREbHIJaIiIiIDIflBERERERkOAxiiYiIiMhwGMQSERERkeEwiCUiIiIiw2EQS0RERESGwyCWiIiIiAyHQSwRERERwWj+H1nKh4x/1DRuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_clusters(pca_result, labels, title):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.scatterplot(x=pca_result[:,0], y=pca_result[:,1], hue=labels, palette='viridis', legend='full')\n",
    "    plt.xlabel('PCA_1')\n",
    "    plt.ylabel('PCA_2')\n",
    "    plt.title(title)\n",
    "    plt.legend(title=\"Cluster\")\n",
    "    plt.show()\n",
    "\n",
    "# 可视化KMeans聚类结果\n",
    "plot_clusters(fp_pca, kmeans_labels, 'KMeans Clustering')\n",
    "\n",
    "# 可视化层次聚类结果\n",
    "plot_clusters(fp_pca, agglo_labels, 'Agglomerative Clustering')\n",
    "\n",
    "# 可视化DBSCAN聚类结果\n",
    "plot_clusters(fp_pca, dbscan_labels, 'DBSCAN Clustering')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4284a966-57ac-492a-a7f3-fcb02a7c4849",
   "metadata": {},
   "source": [
    "### 9.6. 聚类模型效果评价：轮廓系数\n",
    "- 轮廓系数衡量一个数据点\"与自己所在簇的相似度\"相对于\"与其他最近簇的相似度\"的情况。具体来说，对于数据集中的每个点 i，轮廓系数计算如下：\n",
    "\n",
    "    - s(i)= (b(i)−a(i))/max{a(i),b(i)}\n",
    "        - a(i) 是点 i 与同一簇中所有其他点的平均距离（内聚性度量）\n",
    "        - b(i) 是点 i 与最近的不同簇中所有点的平均距离（分离性度量）\n",
    "        - 整个数据集的轮廓系数是所有点轮廓系数的平均值。\n",
    "\n",
    "- 轮廓系数的值范围在 -1 到 1 之间：\n",
    "    - 接近 1: 表示数据点与自己所在的簇非常匹配，并且与相邻簇分离得很好（理想聚类）\n",
    "    - 接近 0: 表示数据点位于簇的边界上，或簇之间的分离不明显\n",
    "    - 接近 -1: 表示数据点可能被分配到了错误的簇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1425c131-4f70-44c7-a307-e304d569fb74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "轮廓系数: 0.05810437383786712\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "from matplotlib import cm\n",
    "\n",
    "silhouette_avg = silhouette_score(fp_arr, kmeans_labels)\n",
    "print('轮廓系数:', silhouette_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "47c1c1c4-9ebc-459d-a2ff-271bb0ef5484",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "轮廓系数: 0.05810437383786712\n"
     ]
    },
    {
     "data": {
      "image/png": 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u2aphQxtNbtMJ1zqfQOvUxp1Oxg4uIa1n1nVp4yCdIK1nYjVo6HeM6Jh8DUf6OstPgtfH0WVkdbiOLsSgr0mfR89u62RmfWxdIjsYCjVgXHPNNabxpYFLe5S0IaQNTK1N5wXovAe9Xm/X+5966qlywAEHmDH+2ljUBpIOh9H9pvtrT7SxrGeWtSGnvSXacNXgoMsi17RYgS7hrEsN6/ex6P7R5ZU15Oly2MH3R896azDVRrTOhdAz6TrvItK5F7pPgt8xogtD6FA8fX5tHFb1vTXh0gayTgjX905r1kauLtoQyZwe3f861Oz//u//zHGt+10bxNpY1euD35dSE33/9D3Xs/r6msoPVQsua609Fjq/TRcF0B4HHaZYm6GWtXkfdR6QzhHS8KPPrftNh5FWnouiS2YHjyV93/V9evLJJ83nSI/Pmugy6fr+6r7QuvQ90FCrx7J+dqqjvTC673Xpal0YQE826D7X77gqT+vR5bL1c6bHoS40oq9Vl84O0tuU7mP9bOvnTuuO5HNcHR1mqGFM/35paNR9p581nfdVfoEJHaarz1f5O4kAxADby7sBqJslpHVp45qWkw1au3atc9xxxzkNGzZ0UlNTzRKrJ554ovPSSy/VWMPdd99tlpUN/l779u2d6dOnO5s3b67VEtLh1rtw4UJn//33N8/ZoEED55RTTnG++eab3erTZXfz8/PN0sY9e/Z0nnvuud2WkA665557zFLN6enpZpncbt26ORdddJFZBrn80s+6xK3ernWVfw333nuvea5AILDbUr66rct067LRaWlpZj+dfvrpzttvv13j/g3uu1dffdWZNGmSU79+fbNssL7eX3/9tcJ9q1oy+8cff3QmTJjgNGrUyOwDfU1VLcW8cuVK89r1PntaTrq6JaTVmjVrzOvUGnXZ5EGDBpnHruo16dLQ4dKlsrt06eIkJSVVOB709Va1hHlV77Euu3zDDTeY++txo/tSX/OVV15Z4XityWWXXWaev0OHDlXerktRH3zwweYYat68uTl+9JirfDxUV/fevo+69LEuda37Xl+fLl/94YcfVthnv/zyi/nbsc8++ziZmZnmmDzooIPMktjh0Mc79thjzdLoeix37tzZmTlzZo2f95KSEufiiy829WtteoysX79+tyWkr7nmGqdPnz7msXUfao3XXnttheXTddnxs88+22ncuLHj8/l2+xsYzudYn7eqpar1750ui6/vne5n/Xfs2LHOZ599VuF+lT/7AGKHT/9jO2gBAGoW/GJDXaJ7Tz0NAAAkOubkAAAAAPAUQg4AAAAATyHkAAAAAPAU5uQAAAAA8BR6cgAAAAB4CiEHAAAAgKdY+TLQ0tJS+e6778wXl+kXlQEAAABITI7jyNatW6V58+bmy8njNuRowGnVqpWNpwYAAAAQg77++mtp2bJl/IYc7cEJvpB69erZKAFu27VLZNy4su0HHxRJS7NdEQAAAOLAli1bTAdIMCPEbcgJDlHTgEPI8YhAQOSFF8q2MzPLLgAAAECY3JzGwsIDAAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAU6wsIQ0P0iWjHcd2FQAAAAA9OQAAAAC8hZADAAAAwFMIOXDHrl0iJ5xQdtFtAAAAwBJCDtxRUiKyaFHZRbcBAAAASwg5AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAU5JsFwCPyMgQ2bbtj20AAADAEkIO3OHziWRm2q4CAAAAYLgaAAAAAG8h5MAdBQUip59edtFtAAAAwBKGq8WR2V/Mlt+KfpNYlLKjSGbff7/ZvvT8HCnMSLZdEhJcs5RmckGbC2yXAQAALCDkxJHPd3wuPxX9JLEodWdxaPuznZ9JAYcWLNtZstN2CQAAwBKGqwEAAADwFEIOAAAAAE8h5AAAAADwFEIOAAAAAE8h5AAAAADwFJbAgisK0gJyystHhrYBAAAAWwg5cIfPJ1sapNquAgAAAGC4GgAAAABvoScHrkgqLJG/3Pyx2f7nhV2kOIUhawAAALCDnhy4IlDiyIhHvzQX3QZs65fbz3YJAADAEkIOAM85JOcQGdtkrO0yAACAJYQcAJ7SNq2tTGs1TXw+n+1SAACAJYQcAJ5RL1BPZrabKWmBNNulAAAAiwg5ADwhIAGZ0XaG5KXk2S4FAABYRsgB4AmTWkySblndbJcBAABiACEHQNwb1mCYHNXoKNtlAACAGMH35MAVhakBOWPx4NA2UFe6ZnaVyS0m2y4DAADEEEIOXOH4ffJTiwzbZSDB5CXnmXk4SX7+lAEAgD8wXA1AXErzp8lf2/1VcpJybJcCAABiDKc/4YqkolI59Y5Pzfb8s/eR4mTyM6LHJz45v9X50i69ne1SAABADKIlClcEikvluAc2mItuA9F0SM4h0je3r+0yAABAjCLkAIg7Y/LG2C4BAADEMEIOgLjSI6uHdMzoaLsMAAAQwwg5AOLKCXkn2C4BAADEOEIOgLjRMb2j9MjuYbsMAAAQ4wg5AOIGc3EAAEA4CDkA4kLL1JZmVTUAAIA94Xty4IrC1IBMWTQwtA1EoxfH5/PZLgMAAMQBQg5c4fh98lWHbNtlwKMaJTeSgbllIRoAAGBPGK4GIOYd2/hYSfJzTgYAAISHVgNckVRUKif8839m+7G/dJTiZPIz3JEdyJahDYfaLgMAAMQRQg5cESgulZPvLgs5T5zenpAD1zRPbS6p/lTbZQAAgDhCSxQAAACApxByAAAAAHgKIQcAAACApxByAAAAAHgKIQcAAACApxByAAAAAHgKS0jDFUUpAZn2YP/QNgAAAGALIQeuKA345H9dc22XAQAAADBcDQAAAIC30JMDVyQVlcrIBRvN9n9PaSfFyeRnuMPPuRgAABAhQg5cESgulT/f9onZXnJSG0IOXDOg/gDbJQAAgDhDSxRAzGqQ1ECGNhhquwwAABBnCDkAYtZxecdJsj/ZdhkAACDOEHIAxKTcpFwZ3nC47TIAAEAcIuQAiEnHNT5OUvwptssAAABxiJADIObkJOXIUY2Osl0GAACIU4QcADFndKPRkupPtV0GAACIUywhDVcUpQTkknsPDm0DtZUdyJajGx1tuwwAABDHCDlwRWnAJx/2bmS7DHjA6MajJS2QZrsMAAAQxxiuBiBmZAWy6MUBAAB7jZ4cuCJQVCpDH//KbD93fGspSSY/I3IjG42UjECG7TIAAECcI+TAFUnFpXLm9R+a7ZdGtSTkIGI+8cmQBkNslwEAADyAliiAmNAtq5s0SmFeFwAA2HuEHAAxYVD9QbZLAAAAHkHIAWBdii9F+uX0s10GAADwCEIOAOsOzjlY0gPptssAAAAeQcgBYN3g+oNtlwAAADyEkAPAqtykXNk/e3/bZQAAAA9hCWm4oijZL1f+vXdoGwjXwNyB4vdxzAAAAPcQcuCK0iS/vD2gie0yEIcGN2CoGgAAcBenTwFY0yatjeSn59suAwAAeAw9OXBFoKhUDlvyrdl+5agWUsKQNYShb05f2yUAAAAPIuTAFUnFpXLerPfM9oohzQg5CEtGIMN2CQAAwINoiQIAAADwFEIOAAAAAE8h5AAAAADwFEIOAAAAAE8h5AAAAADwFEIOAAAAAE9hCWm4oijZL9ffeEBoGwAAALCFkANXlCb55fUhzW2XAQAAADBcDQAAAIC30JMDV/iLS+WQl38w26sGNzU9O8CepPhSbJcAAAA8iJADVyQXlcqMi9aY7TGrhkkBIQdhaJ3W2nYJAADAg2iJArCmXXo72yUAAAAPIuQAsCIvOU8yA5m2ywAAAB5EyAFgBb04AAAgWgg5AKzIT8+3XQIAAPAoQg4AK+jJAQAA0ULIAWBFfho9OQAAIDpYQhquKE7yy21X9ghtAzXJ9GdKk9QmtssAAAAeRciBK0qS/fLSqFa2y0CcaJve1nYJAADAwzjlDqDOsegAAACIJnpy4Ap/cakcsPJns72mb2MpZcgaatA1s6vtEgAAgIcRcuCK5KJSmXXOarM9ZtUwKSDkoIYvAT0452DbZQAAAA+jJQqgTo1sPFL8Pv70AACA6KGlAaDOZPgzZEiDIbbLAAAAHkfIAVBnhjQcIhmBDNtlAAAAjyPkAKgTAQnIMY2OsV0GAABIAIQcAHWiX24/aZzS2HYZAAAgARByANSJ0Y1H2y4BAAAkCJaQhiuKk/xy14yuoW2gvP0y95OOGR1tlwEAABIEIQeuKEn2y5I/tbVdBmIUvTgAAKAuccodQFQ1S2kmB9U7yHYZAAAggdCTA1f4SxzpsuZXs/3xAQ2lNOCzXRJixJENjhSfj+MBAADUHUIOXJFcWCLXTXzDbI9ZNUwK0jm0UKZ7VnfbJQAAgATDcDUAUZPmT5MOGR1slwEAABIMIQdAVFdVC/gCtssAAAAJhpADIGq6ZpYtKw4AAFCXCDkAoqZ7NvNxAABA3SPkAIiKdH+6dEhnPg4AAKh7hBwAUdEls4v4ffyJAQAAdY91fuGKkiS//Pu8fUPbAEtHAwAAWwg5cEVxsl+ePL297TIQQ7pldbNdAgAASFCccgfgugx/hrRPJ/QCAAA76MmBK/wljrT/ZLPZ/nzfHCkN+GyXBItapLZgPg4AALCGkANXJBeWyC3jVpjtMauGSUE6h1YiS/Yn2y4BAAAkME61hmn58uUycuRIad68ufh8PnnqqadslwTErGQfIQcAANhDyAnT9u3bpUePHjJnzhzbpQAxL8WXYrsEAACQwBhTFKbhw4ebC4A9Y7gaAACwiZ4cAK5L8nH+BAAA2EPIAeA6hqsBAACbCDkAXMdwNQAAYBNjSuCKkiS/PDS5Y2gbiY3V1QAAgE2EHLiiONkvD5/Z2XYZiBEpfoarAQAAewg5Ydq2bZusX78+9PPGjRvl3XfflQYNGkjr1q2t1gbEGhYeAAAANtESCdPbb78tgwYNCv08bdo08+/48eNl3rx5kuh8pY602rDNbH+dnyWO32e7JFi0X+Z+tksAAAAJjJATpsMOO0wcx7FdRsxKKSiROWNeNdtjVg2TgnQOrUTVJq2N9MzuabsMAACQwJghDsBVoxqNsl0CAABIcIQcAK7JScqRgfUH2i4DAAAkOEIOANcMbzicldUAAIB1hBwArn03zoiGI2yXAQAAQMgB4I4BuQMkNznXdhkAAACEHADuGN14tO0SAAAADNb5hStKkvzyxGn5oW0klu5Z3aVtelvbZQAAABiEHLiiONkv903rYrsMWEIvDgAAiCWccgewV5qnNJde2b1slwEAABBCTw5c4St1pPH3O832z83SxfH7bJeEOtIqrZX4fLzfAAAgdhBy4IqUghL514iXzfaYVcOkIJ1DCwAAAHYwXA0AAACApxByAAAAAHgKIQcAAACApxByAAAAAHgKIQcAAACApxByAAAAAHgK6/zCFSUBnyw+sU1oGwAAALCFkANXFKcEZO6l3WyXAQAAADBcDcDe8Qk9dwAAILbQkwN3OI7U21RoNrfUTxHx0fBNFCn+FNslAAAAVEDIgStSd5XIgsEvmO0xq4ZJQTqHVqJI86fZLgEAAKAChqsB2Cvp/nTbJQAAAFRAyAGwV+jJAQAAsYaQA2CvEHIAAECsIeQA2CvpAYarAQCA2ELIAbBX6MkBAACxhpADYK+w8AAAAIg1rPMLV5QEfPLSyJahbSQOenIAAECsIeTAFcUpAbnt6p62y4AFzMkBAACxhuFqAPZKZiDTdgkAAAAV0JMDdziOpO4qMZsFaQERH0PWEkGmP1NappYNUwQAAIgV9OTAFRpwFh2y1FyCYQfe1yO7h/h9/BkBAACxhdYJgFrrmcU8LAAAEHsIOQBqbf/s/W2XAAAAsBtCDoBaaZrSVJqmNrVdBgAAwG4IOQBqhaFqAAAgVhFyANQKQ9UAAECsIuQAiJhf/GZlNQAAgFjE9+TAFaV+n6w4olloG97WMaMjXwIKAABiFiEHrihKDcgNNx9ouwzUEYaqAQCAWMZwNQAR657V3XYJAAAA1SLkAIhY27S2tksAAACoFiEHrkjdWSz/7fmMueg2vCsnKUeyk7JtlwEAAFAtQg6AiLRObW27BAAAgBoRcgBEpFVaK9slAAAA1IiQAyAiLVNb2i4BAACgRoQcABGhJwcAAMQ6Qg6AiLROY04OAACIbYQcAGHL8GdIg+QGtssAAACoUVLNNwPhKfX7ZHX/vNA2vImhagAAIB4QcuCKotSAXHVnH9tlIMpapRJyAABA7GO4GoCw+MUvfXIIsgAAIPbRkwNgj9L8aXJRm4ukd73etksBAADYI0IOXJG6s1geHPSC2R637EgpSOfQ8or6SfXl8naXS4eMDrZLAQAACAstUbgmbVeJ7RIQhTk4V+RfIXkpZYtKAAAAxANCDoAqdcvsJpe1u0wyA5m2SwEAAIgIIQfAbgbVHyTntDxHkvz8iQAAAPGHFgyACk7KO0nGNRtnuwwAAIBaI+QAMAISkCktp8iRDY+0XQoAAMBeIeQAkAx/hlzS9hLpmd3TdikAAAB7jZADVzg+n3xwYIPQNuJHo+RGMqvdLGmb3tZ2KQAAAK4g5MAVhWkBufRffW2XgQi1S2sns/JnScPkhrZLAQAAcA0hB0hQKb4UuaHDDZIeSLddCgAAgKv87j4cgHiRk5RDwAEAAJ5EyIErUncWy4ODnjcX3Ubsyw5k2y4BAAAgKhiuBtfkbCq0XQIikJ1EyAEAAN5ETw6QoOjJAQAAXkXIARIUPTkAAMCrCDlAgqInBwAAeBUhB0hQ9OQAAACvIuQACYqeHAAA4FWsrgZXOD6f/K9LTmgbsa9eUj3bJQAAAEQFIQeuKEwLyLSHDrVdBiKQFciyXQIAAEBUMFwNSNChaq3TWtsuAwAAICoIOUACOq3ZaZIZyLRdBgAAQFQQcuCK1J0l8s/hL5mLbiN2dcroJEMbDLVdBgAAQNQwJwcucaTJ9ztD24hNPvHJmS3OFB+LQwAAAA+jJwdIIMMbDpcOGR1slwEAABBVhBwgQeQk5Zi5OAAAAF5HyAESxIRmE1hsAAAAJARCDpAAumR2kcH1B9suAwAAIHYWHtiyZUvYD1ivHt+iDsSSgARYbAAAACSUsEJObm7uHhtIjuOY+5SUsHxwYvLJV/lZoW3EjqMbHS1t09vaLgMAACC2Qs6yZcuiXwniWkF6QKY8cZjtMlBJg6QGckrTU2yXAQAAEHshZ+DAgdGvBIDrDs45WNID6bbLAAAAiP2FB1577TUZN26c9O3bV7799ltz3fz582XFihVu1wcAAAAA0Q05jz/+uAwdOlTS09NlzZo1UlBQYK7fvHmzzJ49O9KHg0ek7iyROce9Yi66DQAAAMRNyLnmmmtk7ty5cu+990pycnLo+n79+pnQg0TlSOsN28xFtwEAAIC4CTnr1q2TAQMG7HZ9Tk6O/P77727VBQAAAAB1E3KaNm0q69ev3+16nY+Tn5/vVl0AAAAAUDchZ+LEiXLuuefKm2++ab4X57vvvpMFCxbIhRdeKGeeeWbtqgAAAACAulxCurwZM2ZIaWmpHH744bJjxw4zdC01NdWEnLPPPtutugAAAACgbkKO9t5cdtllMn36dDNsbdu2bdKlSxfJygp+2z0AAAAAxFHICUpJSZHs7GxzIeBAxCc/Ngt+6aTPci0AAABIZBHPySkuLpaZM2ea1dTatm1rLrr917/+VYqKiqJTJWJeQXpA/vLs4eai2wAAAEDc9OTovJsnnnhCbrzxRjnkkEPMdatWrZIrrrhCfv31V7nrrruiUSeAWmia0tR2CQAAALEfch566CF55JFHZPjw4aHrunfvLq1atZKxY8cScoAY0iO7h+0SAAAAYn+4mq6kpkPUKmvXrp2Zp4PElLKrRG45+TVz0W3Yl5OUI+3S2tkuAwAAIPZDztSpU+Xqq6+WgoKC0HW6fe2115rbkJh8jiMdP95sLroN+7pndTerIQIAACSasIarHXfccRV+fvHFF6Vly5bSo0fZUJj33ntPCgsLzXfnAIgNPbN62i4BAAAgdkOOrp5W3vHHH1/hZ52PAyC29Mwm5AAAgMQUVsi57777ol8JANc0S2kmeSl5tssAAACIjzk5AGJfjyxWVQMAAIkr4iWk1aJFi+TRRx+Vr776yszFKW/NmjVu1Qagllg6GgAAJLKIe3L+/ve/y4QJE6RJkyaydu1a6dOnjzRs2FA2bNhQ4btzkHg2108xF9jlEx89OQAAIKFF3JPzj3/8Q+655x7zxZ/z5s2Tiy66SPLz8+Xyyy+X3377LTpVIuYVpCfJuGVDbJcBEclPz5fspGzbZQAAAMRPT44OUevbt6/ZTk9Pl61bt5rtU089VR5++GH3KwQQkaYpTW2XAAAAEF8hp2nTpqEem9atW8sbb7xhtjdu3CgOXwIJWJfqT7VdAgAAQHyFnMGDB8vTTz9ttnVuzvnnny9HHnmknHTSSXLsscdGo0bEgZRdJTL7jJXmotuwJ82fZrsEAACA+JqTo/NxSktLzfaUKVPMogMrV66UY445RiZPnhyNGhEHfI4j3d75LbQNe+jJAQAAiS7ikOP3+80l6E9/+pO5AIgN9OQAAIBEF1bIef/998N+wO7du+9NPQD2EiEHAAAkurBCTs+ePcXn8+1xYQG9T0kJ8zEAmwg5AAAg0YUVcnTlNADxgTk5AAAg0YUVctq0aRP9SgC4gpADAAASXcQLDwDV2ZUWsF0CGK4GAABAyIE7CtKT5IQ3htsuAyKS7k+3XQIAAEB8fRkogNjVIKmBdM7obLsMAACA+Ak5unLa8uXL5ffff49eRQBq7ehGR0uSnw5aAACQ2CIKOYFAQIYMGSKbNm2KXkWIS8kFJXL51LfMRbdR91J9qTKs4TDbZQAAAFgX8Snfrl27yoYNG6Rdu3bRqQhxyV/qSO8VP4W2UfcGNxgs2UnZtssAAACIvzk511xzjVx44YXyzDPPyPfffy9btmypcAFQ93zik1GNRtkuAwAAID57co466ijz7zHHHCM+ny90veM45medtwOgbh2YfaC0SGthuwwAAID4DDnLli2LTiUAam1UY3pxAAAAah1yBg4cGOmvAIiitmltpWd2T9tlAAAAxPf35Lz22msybtw46du3r3z77bfmuvnz58uKFSvcrg/AHtCLAwAAsJch5/HHH5ehQ4dKenq6rFmzRgoKCsz1mzdvltmzZ0f6cAD2Qm5SrgzMpXcVAABgr4ar6epqc+fOldNOO00eeeSR0PX9+vUztyF6etXrJZuLN0tMyhG5fmNZY7uX7VoS7JhI9ifbLgMAACC+Q866detkwIABu12fk5Mjv//+u1t1oQpntjzTdgkAAACA94arNW3aVNavX7/b9TofJz8/3626AAAAAKBuQs7EiRPl3HPPlTfffNN8L853330nCxYsMF8QeuaZ9DQkrF27RE44oeyi2wAAAEC8DFebMWOGlJaWyuGHHy47duwwQ9dSU1NNyDn77LOjUyVin34J7KJFZdvz5tmuBgAAAAnM5ziOU5tfLCwsNMPWtm3bJl26dJGsrKywf3fLli1mDo+uyFavXr3aPD1izfbtIsFjYNs2kcxM2xUBAAAgDkQjG0Q8XO3Pf/6zbN26VVJSUky46dOnjwk427dvN7cBAAAAgE0Rh5z7779fdu7cudv1et0DDzzgVl0AAAAAEN05OdqNpCPb9KI9OWlpaaHbSkpKZMmSJZKXl1e7KgAAAACgrkNObm6uWU1NL506ddrtdr3+yiuvdKsuAAAAAIhuyFm2bJnpxRk8eLA8/vjj0qBBg9BtOj+nTZs20rx589pVAQAAAAB1HXIGDhxo/t24caO0bt3a9NwAIRkZZauqBbcBAACAeFl44JNPPpHXX3899POcOXOkZ8+ecvLJJ8umTZvcrg/xQkOvLhutFwIwAAAA4inkTJ8+3SxCoD744AOZNm2aHHXUUaaHR7cBAAAAIC6GqwVpmNHvx1E6N2fkyJEye/ZsWbNmjQk7SFAFBSKTJ5dt3323SGqq7YoAAACQoCLuydFFBnbs2GG2X3zxRRkyZIjZ1oUIgj08SEDFxfolSmUX3QYAAADipSenf//+Zlhav3795K233pKFCxea6z/77DNp2bJlNGoEAAAAgOj15Nx5552SlJQkixYtkrvuuktatGhhrn/22Wdl2LBhkT4cAAAAALjK5+iX39QxHdaWk5Mjmzdvlnr16tX10yMatm8Xycoq29alpHWVNQAAAMBCNoh4uNpXX31V4+36HToAAAAAYEvEIadt27Y1fhFoSUnJ3tYEAAAAAHUXctauXVvh56KiInPdLbfcItdee23tKwEAAAAAGyGnR48eu13Xq1cvad68udx0001y3HHHuVEX4k1GhshPP/2xDQAAAMRLyKlO586dZfXq1W49HOKNDmFs3Nh2FQAAAEDkIafyF37q4mzff/+9XHHFFdKxY0c3awMAAACA6Iec3Nzc3RYe0KDTqlUreeSRRyKvAN5QUCAybVrZ9i23iKSm2q4IAAAACSri78l59dVXK/zs9/ulcePG0qFDB/MloeHge3I8iO/JAQAAQLx+T87AgQNdeWIAAAAAiIawQs7TTz8d9gMec8wxe1MPAAAAAEQ/5IwePTqsB9O5OnwZKAAAAICYDzmlpaXRrwQAAAAAXOB340EAAAAAIO5CzssvvyxdunTZ7XtylK6EsN9++8ny5cvdrg8AAAAAohNybrvtNpk4cWKVy7rpkm+TJ0+WW2+9NbJnh3ekp4ts3Fh20W0AAAAg1kPOe++9J8OGDav29iFDhsg777zjVl2IN36/SNu2ZRfdBgAAACwJuzX6448/SnJycrW36xeB/vzzz27VBQAAAADRDTktWrSQDz/8sNrb33//fWnWrFntqkD8KywUmT697KLbAAAAQKyHnKOOOkpmzpwpu3bt2u22nTt3yqxZs+Too492uz7Ei6IikZtvLrvoNgAAAGCJz3EcJ9zhagcccIAEAgGZOnWqdO7c2Vz/6aefypw5c8yXgK5Zs0aaNGmyx8fSFdp0sQJdla2qhQwQh7ZvF8nKKtvetk0kM9N2RQAAAIgD0cgGYX0ZqNLwsnLlSjnzzDPlkksukWA28vl8MnToUBN0wgk4AAAAABBNYYcc1aZNG1myZIls2rRJ1q9fb4JOx44dpX79+tGrEAAAAACiFXKCNNT07t27Nr8KAAAAAFHFF5oAAAAA8BRCDgAAAABPqdVwNWA36ekiwe9R0m0AAADAEkIO3OH3i+y3n+0qAAAAAIarAQAAAPAWenLgjsJCkdmzy7YvvVQkJcV2RQAAAEhQPif4rZ5x/q2msGz7dpGsrLLtbdtEMjNtVwQAAIA4EI1swHA1AAAAAJ5CyAEAAADgKYQcAAAAAJ5CyAEAAADgKYQcAAAAAJ5CyAEAAADgKXxPDtyRliby1lt/bAMAAACWEHLgjkBApHdv21UAAAAADFcDAAAA4C305MAdhYUit99etn3uuSIpKbYrAgAAQILyOY7j1PWTbtmyRXJycmTz5s1Sr169un56RMP27SJZWWXb27aJZGbarggAAABxIBrZgOFqAAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAU/ieHLgjLU1k2bI/tgEAAABLCDlwRyAgcthhtqsAAAAAGK4GAAAAwFvoyYE7iopE7rmnbHvSJJHkZNsVAQAAIEERcuCOwkKRqVPLtk8/nZADAAAAaxiuBgAAAMBTCDkAAAAAPIWQAwAAAMBTCDkAAAAAPIWQAwAAAMBTCDkAAAAAPIUlpOGO1FSRZ575YxsAAACwhJADdyQliYwYYbsKAAAAgOFqAAAAALyFnhy4o6hIZMGCsu1TThFJTrZdEQAAABIUIQfuKCwUmTChbPuEEwg5AAAAsIbhagAAAAA8hZADAAAAwFMIOQAAAAA8hZADAAAAwFMIOQAAAAA8hZADAAAAwFNYQhruSE0VefTRP7YBAAAASwg5cEdSUtn34wAAAACWMVwNAAAAgKfQkwN3FBeLPPlk2faxx5b17AAAAAAW0BKFOwoKRE48sWx72zZCDgAAAKxhuBoAAAAATyHkAAAAAPAUQg4AAAAATyHkAAAAAPAUQg4AAAAATyHkAAAAAPAU1vmFO1JSRO67749tAAAAwBJCDtyRnCxy+um2qwAAAAAYrgYAAADAW+jJgTuKi0Wee65se+hQkSQOLQAAANhBSxTuKCgQOfrosu1t2wg5AAAAsIbhagAAAAA8hZADAAAAwFMIOQAAAAA8hZADAAAAwFMIOQAAAAA8hZADAAAAwFNY5xfuSEkRufPOP7YBAAAASwg5cEdyssiUKbarAAAAABiuBgAAAMBb6MmBO0pKRF57rWz70ENFAgHbFQEAACBBEXLgjl27RAYNKtvetk0kM9N2RQAAAEhQDFcDAAAA4CmEHAAAAACeQsgBAAAA4CmEHAAAAACeQsgBAAAA4CmsrhZHFi5cKFu3bpVYlFRQIKf//+158+ZJcWqq5YoQz1q0aCHDhw+3XQYAAIhThJw48vzzz8tPP/0ksSiptFRK993XbP93yRIp9tNJiNrr3r07IQcAANQaIQeu0FDzZPv2tssAAAAAmJMDAAAAwFvoyYEr/I4j7TdvNtuf5+RIqc9nuyQAAAAkKEIOXJFcUiK3rFhhtscMGyYFSRxaAAAAsIPhagAAAAA8hZADAAAAwFMIOQAAAAA8hZADAAAAwFMIOQAAAAA8hZADIOa054tlAQDAXmCdX7iixO+Xhzp2DG0DtbX//vvL+PHjbZcBAADiGCEHrij2++Xhzp1tl4E417p1a7n44oslEAjYLgUAAMQxTrkDiAk5OTkya9YsyczMtF0KAACIc/TkwBU+x5FW27aZ7a+zssTx+WyXhDiSkpIiM2fOlLy8PNulAAAADyDkwBUpJSUy59VXzfaYYcOkIIlDC+Hx+Xxy/vnnS2eGOwIAAJcwXA2AVePGjZP+/fvbLgMAAHgIIQeANYcffriceOKJtssAAAAeQ8gBYEXXrl1l6tSptssAAAAeRMgBUOeaN28ul156qSQxdwsAAEQBIQdAncrOzjZLReu/AAAA0UDIAVBn0tPTzVLR2pMDAAAQLYwVgStK/H55Ij8/tA1Upl/yeeWVV7JUNAAAiDpCDlxR7PfLfV262C4DMSorK0uuuuoq6dixo+1SAABAAiDkAIgqnXtzzTXXSP7/7+kDAACINkIOXOFzHGm8c6fZ/jk9XRyfz3ZJiAE5OTkm4LRt29Z2KQAAIIEQcuCKlJIS+dfLL5vtMcOGSQFLAye83Nxcufbaa6V169a2SwEAAAmGGeIAXNegQQO57rrrCDgAAMAKQg4AVzVs2FBmz54tLVu2tF0KAABIUIQcAK5p3Lix6cFp0aKF7VIAAEACY+IEAFfk5eWZgKP/AgAA2ERPDoC91qxZM7n++usJOAAAICbQkwMgYikpKeaLPffdd1/ZZ599pGvXrpKZmWm7LAAAAIOQA1eU+HyyuE2b0Da8t1qahhkNNXpp3769JLFMOAAAiFG0UuCK4kBA5nbrZrsMuMDv90ubNm1CgUYvTZo0sV0WAABA2Ag5QILLyMiQzp07hwKNbqenp9suCwAAoNYIOXCH40i9wkKzuSUlRYQhazGradOmFXpptNfGx/sFAAA8hJADV6SWlMiCF14w22OGDZMC5mvEBJ03o/NnunTpEppTU79+fdtlAQAARBUtUcBDcnJyKiwQ0KFDB7MSGgAAQCIh5ABxSoeYtWrVqsLQs+bNm9suCwAAwDpCDhAn0tLSpFOnTqFAoz02fDcNAADA7gg5QIxq3LhxKMzonJp27dqZ5Z0BAABQM0IOEAMCgYDk5+eHAo3+26hRI9tlAQAAxCVCDlCHQSYvL0+aNWsWuugcGr3ol23qSmgAAADYe7Sq4IoSn09eatkytJ2oNKhoYKkqyGjA0aADAACA6CLkRGDOnDly0003yQ8//CA9evSQO+64Q/r06WO7rJhQHAjIbT17SiJITk42X6hZOcTots6jYd4MAACAXYScMC1cuFCmTZsmc+fOlYMOOkhuu+02GTp0qKxbt86coYe36HfLVNUbo9s6V0aXbwYAAEBsIuSE6ZZbbpGJEyfKhAkTzM8adhYvXiz//ve/ZcaMGbbLs89xJLWkxGwW6JCsOAgBuiRzVSFGLw0aNCDIAAAAxClCThgKCwvlnXfekUsuuSR0nQ5JOuKII2TVqlVWa4sVGnAWLV1qtscMGyYFMTKJPiMjo9ogU79+fdvlAQAAIApioyUa43755RcpKSkxE8rL058//fRTa3WhTFZWVrVBJicnx3Z5AAAAqGOEHMSFevXqhUJM+X/1kp2dbbs8AAAAxBBCThh0orku/fvjjz9WuF5/1lW24I7c3Nwqe2P0kpmZabs8AAAAxAlCTpgrbR144IHy0ksvyejRo811paWl5uepU6faLi9u6ER+nQdTVW+MbutCAAAAAMDeIuSESZePHj9+vPTq1ct8N44uIb19+/bQamv4g86Dadq+/W5BRi+pqam2ywMAAIDHEXLCdNJJJ8nPP/8sl19+ufky0J49e8rSpUt3W4wAYr4kNaNxY9tlAAAAIEERciKgQ9MYnla1Up9P3u/cWfbr0kUyWAgAAAAAFhFy4IrOPXpIpyeekADzagAAAGAZIQd7rXv37mYYH/NtAAAAEAsIOdgrOjdp5syZZgU6AAAAIBb4bReA+KXLaocCzvbtukZ02UW3AQAAAEvoyUGt9O7dWy655BJJTk62XQoAAABQASEHETv44IPl4osvlqQkDh8AAADEHlqpiEjfvn1l+vTpBBwAAADELFqqCNuhhx4qF1xwgQQCAdulAAAAANUi5CAsAwYMMAHH72etCgAAAMQ2WqwIa5GBadOmEXAAAAAQF+jJQY323XdfmTFjxp6HqOntRx31xzYAAABgCSEH1WrdurVcfvnl4X3RZ1qayOLFdVEWAAAAUCPGH6FKjRs3lquuukqysrJslwIAAABEhJCD3WRnZ5uA07BhQ9ulAAAAABEj5KCCtLQ0ueKKK6Rly5aR/eL27SKZmWUX3QYAAAAsYU4OQvQLPi+55BLp1KlT7R5gxw63SwIAAAAiRk8ODJ/PJ+edd54ccMABtksBAAAA9gohB8YZZ5whAwcOtF0GAAAAsNcIOZAxY8bIqFGjbJcBAAAAuIKQk+BGjBgh48ePt10GAAAA4BoWHkjg78E566yzpFevXrZLAQAAAFxFyEnABQaOPvpoOe2008xy0a7x+0WCc3p0GwAAALCEkJNAWrVqJeecc47ss88+7j94errIK6+4/7gAAABAhAg5CfL9NyeccIKceOKJZhsAAADwMlq8Hte5c2fTe9O6dWvbpQAAAAB1gpDjUTrfRufd6PwbnYcTddu3i7RtW7b9xRcimZnRf04AAACgCoQcDzrwwAPNyml5eXl1+8S//FK3zwcAAABUgZDjIfXq1ZOJEyfKYYcdZrsUAAAAwBpCjkdosNGAo0EHAAAASGSEHA98qeeUKVPMEDUAAAAAhJy4NmDAADn77LPd/VJPAAAAIM4RcuL4iz3PPfdcSUlJsV0KAAAAEFMIOXFIv9DzwgsvjK2A4/eL9Or1xzYAAABgCSEnDp1yyimSn58vMSU9XWT1attVAAAAAMIp9ziz3377yfHHH2+7DAAAACBmEXLiSEZGhkybNk18Pp/tUgAAAICYRciJI5MmTZK8vDyJSTt2iLRtW3bRbQAAAMAS5uTEiZUrV0qHDh0kZjmOyJdf/rENAAAAWEJPThz47bff5M4777RdBgAAABAXCDkxznEcuf3222Xr1q22SwEAAADiAiEnxi1evFjWrFljuwwAAAAgbjAnJ0Z7b77++msTbubPn2+7HAAAACCuEHJixObNm+Xdd9+VtWvXmovOwwEAAAAQOUKOJUVFRfLJJ5+YQKM9Nhs2bJC4pt/d06XLH9sAAACAJYScOh6CFuyp+eCDD6SwsFA8IyND5KOPbFcBAAAAEHKiPQTtvffeMz01DEEDAAAA6gYhx0WeG4IGAAAAxCFCjotD0D788EMpKCiQhLRjh0jv3mXbq1eXDV8DAAAALCDkRIghaNVwHJGPP/5jGwAAALCEkBPBEDS9fP7557ZLAgAAAFADQk4VQ9C++eabUE9NQg9BAwAAAOIQIafcELRgb82vv/5quyQAAAAAtZSwIae4uFhefPFFWbp0KUPQAAAAAA9JuJBTWloqr776qjz00EPyww8/2C4HAAAAgMuSEmmuzcqVK2XBggVm2We4zOcTadPmj20AAADAkqRECDdvv/22PPjgg3w5ZzTp9+J88YXtKgAAAABvh5z3339f5s+fL59++qntUgAAAADUEU+GHA01Gm405AAAAABILJ4KOTocTYelrV692nYpiWfnTpEBA8q2ly8XSU+3XREAAAASlCdCji4koAsKvP7667ZLSVylpSJvv/3HNgAAAGBJXIccXQJal4J+5ZVXzAIDAAAAABCXIeeXX36RhQsXygsvvCAlJSW2ywEAAAAQQ+Iq5Pz++++yaNEiWbJkiRQVFdkuBwAAAEAMipuQo8PRNOT079/fXBJRbm6u7RIAAACAmBc3Icfn80nbtm1tlwEAAAAgxsVNyEEcaNTIdgUAAAAAIQcuycwU+fln21UAAAAA4rddAAAAAAC4iZADAAAAwFMIOXDHzp0ihx1WdtFtAAAAwBLm5MAdpaUir776xzYAAABgCT05AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAADyF1dXgnowM2xUAAAAAhBy4JDNTZPt221UAAAAADFcDAAAA4C2EHAAAAACeQsiBO3btEhkxouyi2wAAAIAlzMmBO0pKRJYs+WMbAAAAsISeHAAAAACeQsgBAAAA4CmEHAAAAACeQsgBAAAA4CmEHAAAAACeYmV1NcdxzL9btmyx8fSIhu3b/9jW95UV1gAAABCGYCYIZoS4DTlbt241/7Zq1crG0yPamje3XQEAAADizK+//io5OTmuPJbPcTMyham0tFS+++47yc7OFp/Pt1uS0/Dz9ddfS7169eq6tJjFfkEkOF4QLo4VRILjBeHiWEEkNm/eLK1bt5ZNmzZJbm6uxG1Pjt/vl5YtW9Z4H/1A8KHYHfsFkeB4Qbg4VhAJjheEi2MFkWYEt7DwAAAAAABPIeQAAAAA8JSYCzmpqakya9Ys8y/+wH5BJDheEC6OFUSC4wXh4liB7ePFysIDAAAAAJAwPTkAAAAAsDcIOQAAAAA8hZADAAAAwFMIOQAAAAA8JSZCzm+//SannHKK+bIo/ZbTM844Q7Zt21bj79xzzz1y2GGHmd/x+Xzy+++/S7ybM2eOtG3bVtLS0uSggw6St956q8b7P/bYY7LPPvuY+3fr1k2WLFlSZ7Uivo6Xe++9Vw499FCpX7++uRxxxBF7PL7gHZH+bQl65JFHzN/X0aNHR71GxOexov/vnTJlijRr1sysitSpUyf+X5RAIj1ebrvtNuncubOkp6dLq1at5Pzzz5ddu3bVWb2wY/ny5TJy5Ehp3ry5+X/KU089tcffeeWVV+SAAw4wf1c6dOgg8+bNi8+QowHno48+khdeeEGeeeYZszMmTZpU4+/s2LFDhg0bJpdeeql4wcKFC2XatGlm+bw1a9ZIjx49ZOjQofLTTz9Vef+VK1fK2LFjTSBcu3ataYTo5cMPP6zz2hH7x4v+sdDjZdmyZbJq1SrzP5chQ4bIt99+W+e1I7aPlaAvvvhCLrzwQhOOkRgiPVYKCwvlyCOPNMfKokWLZN26deaESosWLeq8dsT+8fLQQw/JjBkzzP0/+eQT+de//mUewyvtOFRv+/bt5vjQUByOjRs3yogRI2TQoEHy7rvvynnnnSd/+ctf5LnnnpOIOJZ9/PHHuoS1s3r16tB1zz77rOPz+Zxvv/12j7+/bNky8/ubNm1y4lmfPn2cKVOmhH4uKSlxmjdv7lx33XVV3v/EE090RowYUeG6gw46yJk8eXLUa0X8HS+VFRcXO9nZ2c79998fxSoRr8eKHh99+/Z1/vnPfzrjx493Ro0aVUfVIp6OlbvuusvJz893CgsL67BKxOvxovcdPHhwheumTZvm9OvXL+q1InZom/3JJ5+s8T4XXXSRs99++1W47qSTTnKGDh0a0XNZ78nRs8o6RK1Xr16h63Qojd/vlzfffFMSgZ4Ne+edd8zrDtLXrz/r/qmKXl/+/krPoFR3fyT28VJVT2hRUZE0aNAgipUiXo+Vq666SvLy8kxPMRJDbY6Vp59+Wg455BAzXK1JkybStWtXmT17tpSUlNRh5YiX46Vv377md4JD2jZs2GCGNh511FF1Vjfig1tt3CSx7IcffjD/My0vKSnJNL70tkTwyy+/mP8p6P8kytOfP/300yp/R/dNVfdPlH2WyGpzvFR28cUXm7Gxlf+IwFtqc6ysWLHCDCPRIQJIHLU5VrSR+vLLL5sh59pYXb9+vZx11lnmBIoOSYJ31eZ4Ofnkk83v9e/fX0cRSXFxsfzf//0fw9UQdht3y5YtsnPnTjOnKxxR68nRcZc6uaimS7gNMgDuuf76682E8ieffNJMFgWCtm7dKqeeeqqZV9GoUSPb5SDGlZaWmpOUuhDQgQceKCeddJJcdtllMnfuXNulIQbp3FDt6fvHP/5h5vA88cQTsnjxYrn66qttlwaPilpPzgUXXCCnn356jffJz8+Xpk2b7jZJTdO9rrimtyUCbUwEAgH58ccfK1yvP1e3D/T6SO6PxD5egm6++WYTcl588UXp3r17lCtFvB0rn3/+uZlErqvglG/IBnvYdWJ5+/bt66ByxMPfFV1RLTk52fxe0L777mvOwupwppSUlKjXjfg5XmbOnGlOougEcqWrwuqEdF1oSsOxDncDamrj6orK4fbiqKgdUY0bNzbLG9d00T+AOp5Xl6DUcZpB2v2t/2PV5QgTge4HPQv20ksvha7T168/6/6pil5f/v5KV6er7v5I7ONF3XjjjeaM2dKlSyvMgYN3RXqs6N/lDz74wAxVC16OOeaY0Ao3uiofvKk2f1f69etnhqgFg7D67LPPTPgh4HhbbY4XnQtaOcgEA3LZfHTA5TauEwOGDRvm7L///s6bb77prFixwunYsaMzduzY0O3ffPON07lzZ3N70Pfff++sXbvWuffee81KDcuXLzc///rrr048euSRR5zU1FRn3rx5ZsW5SZMmObm5uc4PP/xgbj/11FOdGTNmhO7/+uuvO0lJSc7NN9/sfPLJJ86sWbOc5ORk54MPPrD4KhCrx8v111/vpKSkOIsWLTKfneBl69atFl8FYvFYqYzV1RJHpMfKV199ZVZpnDp1qrNu3TrnmWeecfLy8pxrrrnG4qtArB4v2k7R4+Xhhx92NmzY4Dz//PNO+/btzWqx8LatW7eaNrpetM1+yy23mO0vv/zS3K7HiR4vQXp8ZGRkONOnTzdt3Dlz5jiBQMBZunRpRM8bEyFHg4mGmqysLKdevXrOhAkTKjS+Nm7caHaKLhdd/sOi11W+3HfffU68uuOOO5zWrVubxqguzfjGG2+Ebhs4cKBpbJT36KOPOp06dTL316X2Fi9ebKFqxMPx0qZNmyo/L/o5gvdF+relPEJOYon0WFm5cqX5+gJt7Opy0tdee61ZghyJIZLjpaioyLniiitMsElLS3NatWrlnHXWWXH/FSBwwv66l8qX4PGh/+rxUvl3evbsaY4t/dtSm/a9T/8TWd8PAAAAAMQuZnkBAAAA8BRCDgAAAABPIeQAAAAA8BRCDgAAAABPIeQAAAAA8BRCDgAAAABPIeQAAAAA8BRCDgAAAABPIeQA8LRXXnlFfD6f/P7773X2nFdccYX07Nkz9PPpp58uo0ePDv182GGHyXnnnVdn9XiZfp/1pEmTpEGDBuZ9fvfdd6u8LpJ9buOYcdO8efMkNzfXdhkAYBUhB0Dc0oZoTRcNGzZceOGF8tJLL0msq64xH08hbOnSpaZR/8wzz8j3338vXbt2rfK6J554Qq6++uqwHrNv377m93Jyclyr84svvggFLgBA9CXVwXMAQFRoQzRo4cKFcvnll8u6detC12VlZcnbb79d53Xp8+oF0ff5559Ls2bNTDCp6Trt1QlXSkqKNG3a1PVaAQB1h54cAHFLG6LBi5511zPl5a8rHzTeeecd6dWrl2RkZJjGb/kwpP7zn//IAQccIGlpaZKfny9XXnmlFBcX19gL0qdPH8nMzDRDg/r16ydffvlllcPVqlJaWioXXXSRaXxrrZV7nb766isZNWqUeQ316tWTE088UX788cdqh8Ap7X3RXpjyz3HddddJu3btJD09XXr06CGLFi0K9SwMGjTIbNevX9/sO31Mvbz66qty++23h3rE9L7qww8/lOHDh5uamjRpIqeeeqr88ssvNb7O119/3dSk+12fZ+jQobJp0yZzW0FBgZxzzjmSl5dn9nv//v1l9erVFX6/pufUWs8++2yzr7TOtm3bVnldVb1T+twXX3yxtGrVSlJTU6VDhw7yr3/9q9oerhUrVsihhx5q9qP+jta9ffv20O36PLNnz5Y///nPkp2dLa1bt5Z77rkndLu+B2r//fc3j13+fSr/frVs2VLuuuuuCtevXbtW/H5/6Pi65ZZbpFu3bubY01rOOuss2bZtW7Xvwd4eK0rfs1NOOUUaN25sbu/YsaPcd9991T4nANhGyAGQEC677DL529/+Znp2kpKSTGM06LXXXpPTTjtNzj33XPn444/l7rvvNsOdrr322iofS8OPNhoHDhwo77//vqxatcrMAdHGa7juv/9+00h988035cYbb5SrrrpKXnjhhVCDUwPOb7/9ZgKHXr9hwwY56aSTInrN2mh94IEHZO7cufLRRx/J+eefL+PGjTOPqY3jxx9/3NxPA5/2immw0cshhxwiEydONNfpRe+rDf7BgwebRrruQx0SpqFLw1d1dGjW4YcfLl26dDH7SIPCyJEjpaSkxNyuIU9r0H2xZs0aEzQ0BOnrVnt6Tq1V95sGA61TA1JV11VF3++HH35Y/v73v8snn3xi3vPqet+0Z2jYsGFy/PHHm/dbew31tUydOrXC/fT40iCtoUSDx5lnnhkK02+99Zb598UXXzR16fC5yjTIjB07Vh566KEK1y9YsMCE6DZt2oTup3Xre6r77uWXXzb7cm/UdKyomTNnms/Gs88+a/aXBrFGjRrt1XMCQFQ5AOAB9913n5OTk7Pb9cuWLXP0T92LL74Yum7x4sXmup07d5qfDz/8cGf27NkVfm/+/PlOs2bNqnyuX3/91fz+K6+8UuXts2bNcnr06BH6efz48c6oUaNCPw8cONDp379/hd/p3bu3c/HFF5vt559/3gkEAs5XX30Vuv2jjz4yz/nWW29V+Zjq3HPPNY+tdu3a5WRkZDgrV66scJ8zzjjDGTt2bIV9s2nTpgr30cfQxyrv6quvdoYMGVLhuq+//tr8/rp166rcD/o8/fr1q/K2bdu2OcnJyc6CBQtC1xUWFjrNmzd3brzxxrCf89Zbb3XatGlT4T5VXVf+Nenv6mO88MILVdZWeb/oPps0aVKF+7z22muO3+8PHUP6fOPGjQvdXlpa6uTl5Tl33XWX+Xnjxo3mMdeuXevURG/3+XzOl19+aX4uKSlxWrRoEXqcqjz22GNOw4YNq/0suHGsjBw50pkwYUKNtQNALGFODoCE0L1799C2ztdQP/30kxlW9N5775lhVeV7brS3YdeuXbJjxw4z1Ko8HWKmQ4C01+HII4+UI444wvQuBB830nqCNWk9Ss+Ua++JXoK0N0SHxeltvXv33uPjr1+/3tSu9ZVXWFhoekYipfto2bJlVfZ2aE9Hp06dquzJOeGEE6p8PP2doqIi00MRlJycbIYA6mus7XOGQ+sKBAKmJy4cWof24GiPSpCu4KY9bhs3bpR99913t/c0OHQy+J6GS4c56uNpb86MGTNMT4o+Rvn9qL1B2vPy6aefypYtW0zPYnXHajjCOVa0V0p7srTHbciQIaYns/ycJwCINYQcAAlBG9BBwWFl2khVOp9B5+Acd9xxu/2ezhWpis5H0HkZOoRKhy/99a9/NcPKDj744IjrCdYUrCccOmRJG9rlaWgICs7RWLx4sbRo0aLC/XQOSqT08XSo2Q033LDbbdWFO527sTdq85zhiLQurWPy5Mnm/a5MQ7Jb72mQzn0Jhhz9V4fKNWzY0Nym86OOPvpoEzo0lGvg1qFzZ5xxhgklVYUcN44VnRelc4KWLFlijnMdhjhlyhS5+eabI359AFAXCDkAEp4uOKBzJ3ROSCT0LLdeLrnkEjOPRRuk4YacmuiZ/K+//tpcgr05Oh9C56hoj47SCeA6Kb9yD0Wwoa330waqTsCvrsdCVxFTwTky5a+vfJ3uI50/oxPsdU5TOLRnQ5fS1gBZWfv27c3zaA9acK6JNrx1Dk1wgYDaPGc4dNK+hg/tJdFeuD3ROnT/R3p8hLOvq3LyySeb0KyLZejkf50nE6TXae06/0fDi3r00UdrfDw3jpXg44wfP95cdBGG6dOnE3IAxCwWHgCQ8HTpaZ10rY1xnXStw6UeeeQR09Csig5R0mCjk+n17Pbzzz8v//vf/0LDlvaWNry1Ia5n9HV4kE5a14ny2gDVie1KJ+TrZHytW5971qxZFRqyusKXfl+PTiDXyek6vEsf64477jA/Kw0X2tug3yfz888/h87oa6jQBRG010BXMtNGtZ611wUBdGK8BhF9vOeee04mTJhQbcNd95HeVyfh63AvHV6lE9b1MXXRBe2N0Iay9oZpiNDFDnTYlPZKqNo8Zzj09WlDXRefeOqpp8z7qSuqVRcWdBW2lStXmoUGNBzo/tbV+CovPFATXUFOe5CCiyds3ry5xvp0KJjuB32dxxxzTOg2DVoaBvV91MUo5s+fXyEEVcWNY0U/I/qadWibfkb0mHHreAeAaCDkAEh4OrdGG20aVnS+i/bG3HrrraEehsp0SJA22HWOgs4L0ZXVtEGuQ5rcoMFDG5S65PKAAQNM6NFlrXVYXPmadcUrXVVLa966dasJQuXpl1/qfXT+hjZIddiTDkkKLmesQ5M02OmwKF2eOdho1wavzlnRM/x69l7P8Ddv3tz0umijW+dkaAjTHhedJxTsUahM943uU53TonNttLdLX1ewV+b66683+1CXhdbeEm1Aa4jR161q85zh0rA1ZswYE8D22WcfE7DKLwlduUdKe30+++wz04OhvXfa6Nf6wqWvWVdE01Xc9Pd09byaaMDV/XbsscdWGF6nSzvrEtI6hE+/5FTnCen7WxM3jhXtidLQqvtCj0k9PvREAADEKp+uPmC7CAAAAABwCz05AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAADyFkAMAAADAUwg5AAAAAMRL/h+mefj0ZOCumAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 轮廓系数绘图\n",
    "# 计算轮廓系数\n",
    "silhouette_avg = silhouette_score(fp_arr, kmeans_labels)\n",
    "print('轮廓系数:', silhouette_avg)\n",
    "\n",
    "# 绘制轮廓图\n",
    "plt.figure(figsize=(10, 7))\n",
    "sample_silhouette_values = silhouette_samples(fp_arr, kmeans_labels)\n",
    "\n",
    "y_lower = 10\n",
    "for i in range(2):  # 假设聚类数目为2\n",
    "    ith_cluster_silhouette_values = sample_silhouette_values[kmeans_labels == i]\n",
    "    ith_cluster_silhouette_values.sort()\n",
    "    size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "    y_upper = y_lower + size_cluster_i\n",
    "\n",
    "    color = cm.nipy_spectral(float(i) / 2)\n",
    "    plt.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                      ith_cluster_silhouette_values,\n",
    "                      facecolor=color, alpha=0.7)\n",
    "    plt.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "    y_lower = y_upper + 10\n",
    "\n",
    "plt.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "plt.title(\"The silhouette plot for the various clusters.\")\n",
    "plt.xlabel(\"The silhouette coefficient values\")\n",
    "plt.ylabel(\"Cluster label\")\n",
    "plt.yticks([])\n",
    "plt.xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a80b5d-2770-4918-b4c7-ecbddaaa5bfa",
   "metadata": {},
   "source": [
    "#### 9.6.1. 优化方案\n",
    "- 尝试使用不同的n_clusters，然后对比轮廓系数\n",
    "    - 注意定住random_state为一个定制，避免由于随机化导致的差异"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f940e0c8-37cb-4037-9a02-e98d761c4ec1",
   "metadata": {},
   "source": [
    "### 9.6. 固化成形：做成函数调用\n",
    "- 只要提供了分子特征矩阵（numpy二维数组）和聚类目标的簇数，即可进行聚类分析\n",
    "- 打包成函数，放入py文件\n",
    "- 脚本调用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d519b3b-afa4-4500-b3d5-ae91f91385e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "94076766-a8e8-4175-bdc8-907d548414ff",
   "metadata": {},
   "source": [
    "### 9.7. 使用相似度矩阵进行聚类分析"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
